Device for detection of cellular stress

ABSTRACT

Disclosed is an assay for determining resistance in a target cell or tissue to a therapy associated with cellular stress using chemical microscopy and high-throughput single cell analysis to determine functional metabolic alteration, including determining metabolic reprogramming in a target cell or tissue to a therapy associated with cellular stress, and methods of using the assays.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims benefit under 35 U.S.C. § 119(e) of U.S.Provisional Application No. 63/210,286, filed Jun. 14, 2021, thecontents of which are incorporated herein by reference in theirentirety.

GOVERNMENT SUPPORT

This invention was made with government support under Contract No.CA224275 awarded by the National Institutes of Health. The governmenthas certain rights in the invention.

SEQUENCE LISTING

The instant application contains a Sequence Listing that has beensubmitted electronically in ASCII format and is incorporated byreference in its entirety. The ASCII copy, created on Jun. 8, 2022, isnamed BOS-0021US-SEQLIST_ST25.txt and is 5,000 bytes in size.

TECHNICAL FIELD

The disclosed technology relates to assays for determining resistance ina target cell or tissue to a therapy associated with cellular stress,and methods of using the assays.

BACKGROUND

Metabolic reprogramming in cancer cells has been recognized since thediscovery of the Warburg effect in 1920s [1, 2]. Increased aerobicglycolysis is now widely considered as a hallmark of many cancers andclinically exploited as a target for cancer therapy and a cancerbiomarker for diagnosis [3]. In the past decade, numerous studies haveinvestigated the heterogeneity and complexity of cancer metabolismbeyond the Warburg effect [4]. Metabolic reprogramming allows cancercells to adapt to intrinsic or extrinsic cues from the microenvironmentthrough plasticity and high flexibility in nutrient acquisition andutilization [5]. Particular attention has been paid to metabolicalterations associated with critical steps of cancer progression, suchas metastasis initiation, circulation and colonization [5-7]. Metabolicreprogramming in cancer stem cells identified potential vulnerabilitiesfor cancer stem cells targeting therapy [8, 9]. Cancer cells also rewiretheir metabolic dependencies within a specific microenvironment niche byinteracting with stroma cells [10] or with the surrounding adipocytes[11, 12]. Further, alterations in nutrient utilization under metabolicstress conditions have recently been reported [13-15]. Despite theserecent advances, the understanding of cancer cell metabolism remainsincomplete. One of the less studied areas is cancer metabolicreprogramming associated with resistance to therapy.

Therapeutic resistance remains one of the biggest challenges facingcancer treatment. Resistance to chemotherapy or molecularly targetedtherapies is a major cause of tumor relapse and death [16]. Emergingstudies support an association between metabolic reprogramming andcancer drug resistance [17, 18]. Several studies have linked the Warburgeffect to resistance to radiation [19] and lactate production was shownto promote resistance to chemotherapy in cervical cancer [20]. Alteredlipid metabolism has also been implicated in acquisition of drugresistance [21]. Increased de novo lipogenesis mediated by FASNfacilitated gemcitabine resistance in pancreatic cancer [22] whilecancer associated adipose tissue promoted resistance to antiangiogenicinterventions by supplying fatty acid to cancer cells in regions wherethe glucose demand was insufficient [23]. Additionally, lipid dropletproduction mediated by lysophosphatidylcholine acyltransferase 2promoted resistance of colorectal cancer cells to 5-fluorouracil andoxaliplatin [24]. It has been proposed that drug tolerant cells adopt astate of diapause similar to suspended embryonic development to survivechemotherapy toxic insults, in which cell proliferation and metabolicprocesses are suppressed [25]. These studies support that metabolicreprograming underlie development of drug resistance and point topotential metabolic vulnerabilities of resistant cancer cells, whichremain underutilized.

Platinum-based drugs, including cisplatin, carboplatin and oxaliplatin,represent one class of the most widely used chemotherapy drugs [26].Resistance to platinum is a barrier to effective treatment in multiplecancers, including ovarian, testicular, bladder, head and neck,non-small-cell lung cancer and others [27]. Understanding the metabolicreprograming underlying platinum resistant cancer cells is critical fordevelopment of effective treatment strategies. Yet, precisely profilingmetabolic reprogramming using conventional technology is difficult,because within a cell population, only a small portion of cells is drugresistant or tolerant. In this study, by taking advantage of ahyperspectral stimulated Raman scattering (SRS) imaging platform, wedepict the metabolic profile of platinum resistant cancer cells at thesingle cell level.

SRS microscopy is a recently developed label-free chemical imagingtechnique that detects the intrinsic chemical bond vibrations [28-31].The value of SRS microscopy was demonstrated in identifying cholesterylester accumulation as a signature associated with multiple aggressivecancers [32, 33], discovering increased lipid desaturation in OC stemcells, and tracing metabolic flux by isotope labeling [34-36]. Morerecently, large-area hyperspectral SRS microscopy and high-throughputsingle cell analysis revealed lipid-rich protrusion in cancer cellsunder stress [37]. Raman spectro-microscopy based single cellmetabolomics unveiled an important role of lipid unsaturation inaggressive melanoma [38]. This technology holds promise forunderstanding aerobic glycolysis and lipid metabolism associated withcellular stress.

SUMMARY

This disclosure provides an assay for determining resistance in a targetcell or tissue to a therapy associated with cellular stress orperturbation, and methods of using the assay. One aspect of thedisclosure is an assay for determining resistance in a target cell ortissue to a therapy associated with cellular stress comprising measuringwith chemical microscopy a functional metabolic alteration or change inthe target cell or tissue, and determining a metabolic index ofresistance in the target cell or tissue to the therapy. The functionalmetabolic alteration or change is a change from glucose and glycolysisdependent anabolism and energy metabolism to fatty acid uptake and fattyacid oxidation dependent anabolism and energy metabolism. Inembodiments, the metabolic index correlates to resistance to a therapyin the target cell when the metabolic alteration or change is a decreasein glucose and glycolysis dependent anabolism and an increase in fattyacid uptake and fatty acid oxidation dependent anabolism and energymetabolism. In some embodiments, the metabolic index further correlatesto resistance to the therapy in the target cell when the metabolicchange is a decrease in de novo lipogenesis in the target cell.

Another aspect of the disclosure is use of the disclosed assay inmethods of treating or inhibiting resistance in a target cell or tissueto a therapy associated with cellular stress. Embodiments include amethod of treating or inhibiting resistance in a target cell or tissuein a subject to a therapy associated with cellular stress in a subjectby performing an assay as disclosed herein to determine a metabolicindex of resistance in the target cell to the therapy, administering atleast one inhibitor of fatty acid oxidation to the subject, andadministering at least one therapy to the subject.

One embodiment of the method comprises measuring with chemicalmicroscopy a functional metabolic change in glucose and glycolysisdependent anabolism and an increase in fatty acid uptake and oxidationin which the glucose and glycolysis dependent anabolism decreases andfatty acid uptake and oxidation increases.

In the disclosed embodiments, the target cell is a cell that may undergometabolic reprogramming or alteration in response to cellular stress,such as a cancer cell, an immune cell, or a benign neoplasm. In theembodiments, the target cell is a cancer cell, for example ovarian,prostate, testicular, bladder, pancreatic, lung, breast, esophageal,head, and neck cancer. In the embodiments, the therapy is a cancertherapy that induces a metabolic alteration in the cell.

Other features and advantages of aspects of the disclosure will becomeapparent from the following more detailed description, taken inconjunction with the accompanying drawings, which illustrate, by way ofexample, the principles of aspects of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The teachings in the present disclosure will be more fully understoodfrom the following description of various illustrative embodiments, whenread together with the accompanying drawings. It should be understoodthat the drawings described below are for illustration purposes only andare not intended to limit the scope of the present teachings in any way.

FIGS. 1A-1Q illustrate high-throughput profiling of lipid metabolism inovarian cancer cell lines. FIG. 1A is an image of processing flow forhigh-throughput single cell analysis of lipids using hyperspectralstimulated Raman scattering (SRS) imaging; lipids were color-coded basedon the colors of their parental cells; quantitative analysis of cellmorphology, lipid quantity and intensity were also produced along withthe images; image area is 500 μm by 500 μm. FIGS. 1B-1E showdose-response to cisplatin in PEO1 and PEO4 (FIG. 1B), SKOV3 andSKOV3-cisR (FIG. 1C), OVCAR5 and OVCAR5-cisR (FIG. 1D), COV362 andCOV362-cisR cells (FIG. 1E); n=6 biological replicates. FIG. 1F showsrepresentative large-area SRS images of parental PEO1 andcisplatin-resistant PEO4 cells (isogenic pairs of cisplatin-sensitiveand cisplatin-resistant ovarian cancer (OC) cells). FIG. 1G showshistograms of integrated cellular lipid intensity in PEO1 and PEO4 cellsgenerated through high-throughput single-cell analysis. FIG. 1H showsrepresentative large-area SRS images of parental SKOV3 andcisplatin-resistant SKOV3-cisR cells. FIG. 1I shows a histogram ofintegrated cellular lipid intensity in SKOV3 and SKOV3-cisR cells. Datafor FIGS. 1F-1I are presented as mean+SD; n=3 animals; two-sidedStudent's t test; P=0.043; * P<0.05. All scale bar: 20 μm. FIGS. 1J-1Kshow histograms of integrated cellular lipid intensity in OVCAR5 andOVCAR5-cisR (FIG. 1J), and in COV362 and COV362-cisR cells (FIG. 1K).FIGS. 1L-1M show histograms of integrated cellular lipid intensity inSKOV3 cells (FIG. 1L) and in SKOV3-cisR cells (FIG. 1M) treated with orwithout cisplatin. Data are presented as mean +SD; n=3 animals;two-sided Student's t test; P=0.043; * P<0.05. All scale bar: 20 μm.FIG. 1N illustrate weights of xenografts from mice treated with salineor carboplatin for 3 weeks; (n=4, two-sided Student's t test; P=0.030; *P<0.05). FIG. 1O shows a dose-response to carboplatin in OC cellsderived from xenografts developed in mice treated with carboplatin orsaline; the dose-response curves and scatter plot are shown as means±SD,n=4 technical replicates. FIG. 1P presents a representativehyperspectral SRS image (sum of all channels) and Phasor mapped lipidimage of sliced OVCAR5 xenograft tumor tissue from mouse treated withvehicle (sensitive) or carboplatin (resistant). FIG. 1Q showsquantitative analysis of SRS signal from lipid in carboplatin sensitiveand resistant ovarian tumor tissue by area fraction. Data for FIGS.1P-1Q are presented as mean+SD; n=3 animals; two-sided Student's t test;P=0.043; * P<0.05. All scale bar: 20 μm.

FIGS. 2A-2M illustrate increased fatty acid uptake, not de novolipogenesis, is the major contributor to lipid accumulation incisplatin-resistant OC cells. FIG. 2A shows representative bright fieldand SRS images of PEO1 and PEO4 cells fed with glucose-d₇ for 3 days.FIG. 2B shows quantitative analysis of SRS signal of C-D bonds inglucose-d₇ fed PEO1 and PEO4 cells by mean intensity and area fraction;n=5. P=0.0076 and 0.0083. FIG. 2C shows representative bright field andSRS images of PEO1 and PEO4 cells fed with PA-d₃₁ for 6 h. FIG. 2D showsquantitative analysis of SRS signal of C-D bonds in PA-d₃₁ fed PEO1 andPEO4 cells by mean intensity and area fraction; n=6. P=0.0051 and3×10⁻⁵. FIG. 2E presents representative bright field and SRS images ofPEO1 and PEO4 cells fed with OA-d₃₄ for 6 h. FIG. 2F shows quantitativeanalysis of SRS signal of C-D bonds in OA-d₃₄ fed PEO1 (n=6) and PEO4(n=7) cells by mean intensity and area fraction; P=0.030 and 0.0048.FIG. 2G presents representative SRS images of SKOV3 and SKOV3-cisR cellsfed with glucose-d₇ for 3 days and quantitative analysis of SRS signalof C-D bonds by mean intensity. FIGS. 2H-2I present representative SRSimages of SKOV3 and SKOV3-cisR cells fed with PA-d₃₁ for 6 h (FIG.2H)(n=5. P=0.0010) and fed with OA-d₃₄ for 6 h and quantitative analysisof SRS signal of C-D bonds by mean intensity(FIG. 2I)(n=8. P=2.2×10⁻⁵).The results in all the bar charts are shown as means+SD. All statisticalsignificance was analyzed using one-sided Student's t test. * P<0.05, **P<0.01, and *** P<0.001. All scale bar: 20 μm. FIGS. 2J-2K presentrepresentative bright field and SRS images of OVCAR5 and OVCAR5-cisRcells (FIG. 2J) and of COV362 and COV362-cisR cells (FIG. 2K), fed withglucose-d₇ for 3 days, PA-d₃₁ for 6 h, or OA-d₃₄ for 6 h; n=6. FIG. 2Lshows representative sum of hSRS and phasor mapped lipid image of SKOV3and SKOV3-cisR cells treated with vehicle or 10 μM C-75. FIG. 2M showsquantitative analysis of SRS signal from lipid in SKOV3 and SKOV3-cisRcells treated with vehicle or 10 μM C-75. The results are shown asmeans+SD, n=6-8. * P <0.05, ** P<0.01, and *** P<0.001.

FIGS. 3A-3N show that metabolic index calculated by integrating glucosederived lipogenesis and fatty acid uptake directly correlates withcisplatin resistance. FIGS. 3A-3C present linear regression ofglucose-d₇ intensity to IC_(50S) of cisplatin in various OC cell lines(FIG. 3A), of PA-d₃₁ intensity to IC_(50S) of cisplatin in various OCcell lines (FIG. 3B), and of PA-d₃₁/(PA-d₃₁+Glucose-d₇) to IC_(50S) ofcisplatin in various OC cell lines (FIG. 3C). FIG. 3D shows normalizedSRS spectra of 17-Octadecynoic Acid (ODYA) and glucose-d7 in cells. FIG.3E presents output SRS spectra from phasor analysis of C≡C bonds fromODYA and C-D bonds from glucose-d₇ and metabolites. FIG. 3F presentsrepresentative bright field images, raw SRS images, and processed SRSimages of ODYA and glucose-d₇ in OVCAR5 and OVCAR5-cisR cells. FIG. 3Gshows quantitative analysis of ODYA derived C≡C intensity (n=4),glucose-d₇ derived C-D intensity (n=6), and the ratio of C≡C/(C≡C+C−D(n=4)) in OVCAR5 and OVCAR5-cisR cells, P=0.0082. FIG. 3H presentsrepresentative bright field images, raw SRS images, and processed SRSimages of ODYA and glucose-d₇ in PEO1 and PEO4 cells; scale bar 20 μm.FIG. 3I shows quantitative analysis of ODYA derived C≡C intensity,glucose-d₇ derived C-D intensity, and the ratio of C≡C/(C≡C+C−D) in PEO1(n=6) and PEO4 cells (n=7). The results in all the bar charts are shownas means+SD. All statistical significance was analyzed using one-sidedStudent's t test. * P<0.05, ** P<0.01, and *** P<0.001. FIG. 3J showslinear regression of the metabolic index, as defined by the ratio ofC≡C/(C≡C+C−D) to IC_(50S) of cisplatin in various OC cell lines;R²=0.9235; n=6 for (a-c) and (e). FIG. 3K shows representative brightfield images, raw SRS images, and processed SRS images of ODYA andglucose-d₇ in primary OC cells from cisplatin treatment resistantpatients and from the cisplatin treatment sensitive patient. FIG. 3Lshows quantitative analysis of metabolic index (the ratio ofC≡C/(C≡C+C−D) for primary OC cells from cisplatin treatment resistantpatients and from the cisplatin treatment sensitive patient; each datapoint represents the average metabolic index of individual cancer cellsfrom a patient and its error bar indicates the SEM; n=30, 31, 19, 25,27, 33, 12, 11, 24, 20 and 30. The box plot indicates analysis of eachgroup (sensitive (n=7) v.s. resistant (n=4)). The bound of outer boxrepresents SEM; inner box indicates mean; lines represent medium;whiskers indicate 25% to 75% of data; circles indicate maxima and minimaof data. The statistical significance was analyzed using two-sidedStudent's t test; P=0.011. * P<0.05. All scale bar: 20 μm. FIG. 3M showshistograms of metabolic index of primary ovarian cancer cells fromplatinum resistant patients and platinum sensitive patients; n=4. FIG.3N is a receiver operating characteristic (ROC) curve for metabolicindex of primary ovarian cancer cells from patients with platinumresistant or platinum sensitive tumors. AUC: area under curve.

FIGS. 4A-4DD show that fatty acid uptake directly contributes tocisplatin resistance. FIG. 4A is a SRS image of OVCAR5-cisR cellcultured with control serum (FBS), delipid serum or control serumsupplemented with 1% lipid mixture for 24 hours. FIG. 4B showsquantitative C-H signal from lipid droplet in FIG. 1A (OVCAR5-cisR cellcultured with control serum (FBS), delipid serum, or control serumsupplemented with 1% lipid for 24 hours). FIG. 4C shows representativeSRS images of SKOV3-cisR cell cultured with control serum (FBS) (n=5),no serum (n=6) and control serum supplemented with 1% lipid mixture(n=6) for 24 hours. Scale bar: 20 μm. FIG. 4D shows quantitative C-Hsignal from lipid droplet in FIG. 4C; P=0.044 and 0.00089. FIG. 4E showsa dose-response to cisplatin under culture environment with control,reduced (medium containing delipid serum or no serum) and increased(control serum supplemented with 1% lipid mixture) lipid content forOVCAR5-cisR cells; n=3 biological replicates. FIGS. 4F-4G showdose-response to cisplatin under culture environment with control,reduced (medium containing delipid serum or no serum) and increased(control serum supplemented with 1% lipid mixture) lipid content forPEO1 (FIG. 4F), and for SKOV3 (FIG. 4G); n=3. FIG. 4H showsrepresentative bright field and fluorescent images of SKOV3 (n=19 and20) and SKOV3-cisR cells (n=16) treated with 100 μM fluorescent glucoseanalog 2-deoxy-2-[(7-nitro-2,1,3-benzoxadiazol-4-yl) amino]-D-glucose(2-NBDG) for 2 hours after cultured in control or reduced lipid contentmedium (delipid serum) for 24 hours; scale bar: 50 μm. FIG. 4I showsquantified fluorescent signal intensity for FIG. 4H; P=0.024. FIG. 4Jrepresents relative mRNA expression level of CD36, FATP1-6, FABP4-5, andFABP PM in OVCAR5 and OVCAR5-cisR cells; n=2 for FATP6; n=4 for FABP4and FATP4; n=3 for other genes; n represents biological replicates.FIGS. 4K-4L show relative mRNA express levels of FABP5 (FIG. 4K) andFABP(PM) (FIG. 4L) in OVCAR5 and OVCAR5-cisR cells; the results areshown as means+SD; n=4 biological replicates. P=0.0037 and 0.0018. FIG.4M shows relative mRNA express level of FABP5 in PEO1 (n=4) and PEO4(n=5) cells; P=0.012. FIG. 4N shows relative mRNA express level FABP5and FABP(PM) in OVCAR5 cells treated with cisplatin for 0, 6, 12 or 24hours; n=3. P=0.00016, 2.4×10⁻⁶, 0.00037, 0.0069, 0.00037 and 0.052.FIGS. 4O-4P show relative mRNA expression levels of GLUT1 in SKOV3 andSKOV3-cisR cells (FIG. 4O)(n=8. P=0.0089), and in OVCAR5 cells treatedwith cisplatin for 0, 6, 12 or 24 hours; n=3 (FIG. 4P). FIGS. 4Q and 4Sshow representative bright field and SRS images of SKOV3 and SKOV3-cisRcells fed with PA-d₃₁ (FIG. 4Q) and OA-d₃₄ (FIG. 4S) in variousconcentrations for 6 h; scale bar: 20 μm. FIGS. 4R and 4T showquantitative analysis of SRS signal of C-D bonds in PA-d₃₁ (FIG. 4R) orOA-d₃₄ (FIG. 4T) fed SKOV3 and SKOV3-cisR cells by mean intensity; n=38,33, 21, 37, 35, 35, 42, and 23. P=2.2×10⁻⁷, 0.0016, and 0.019. Data inbar charts FIGS. 4D, and 4J, 4M, 4O, and 4P are presented as means+SD.Data in dose-response curveS FIGS. 4F and 4G are presented as mean±SD.For box plots FIGS. 4I, 4R, and 4T, the bound of outer box indicates 25%to 75% of data; inner box indicates mean; lines represent medium;whiskers indicate SD; circles indicate maxima and minima of data. Allstatistical significance was analyzed using one-sided Student's ttest. * P<0.05, ** P<0.01, and *** P<0.001. FIG. 4U presentsrepresentative bright field and SRS images of OVCAR5-cisR cell afterfatty acid (FA) transporter inhibitor BMS309403 (BMS) treatment at 10 μMfor 24 hours during concomitant incubation with 100 μM PA-d₃₁ for 6hours (BMS inhibits FA uptake and sensitizes OC cell to cisplatintreatment). FIG. 4V shows quantification of C-D SRS signal intensity forFIG. 4U; n=9 and 8. P=0.0026. FIG. 4W is representative bright field andSRS images of OVCAR5-cisR cell after FA transporter inhibitor BMStreatment at 5 μM, 10 μM or 20 μM for 24 hours with the incubation of100 μM PA-d₃₁ for 6 hours; scale bar: 20 μm. n=6, 5, 6 and 6 technicalreplicates. FIG. 4X shows quantification of C-D SRS signal intensityfrom OVCAR5-cisR after treatment with BMS at 5 μM, 10 μM, or 20 μM for24 hours during concomitant incubation of 100 μM PA-d31 for 6 hours;data are presented as means+SD; n=6, 5, 6 and 6 technical replicates;one-sided Student's t test; P=0.019, 0.0055 and 0.0056. * P<0.05, **P<0.01. FIGS. 4Y-4DD show dose-response to cisplatin with or withoutsupplemental BMS treatment for PEO4 (FIG. 4Y), SKOV3-cisR (FIG. 4Z), andOVCAR5-cisR (FIG. 4AA) cells, as well as PEO1 (FIG. 4BB), SKOV3 (FIG.4CC) and OVCAR5 (FIG. 4DD) cells. The results in all the dose-responsecurves are shown as means±SD; n=3 biological replicates. Data in all thebar charts are shown as means+SD. All statistical significance wasanalyzed using two-sided Student's t test. ** P<0.01, and *** P<0.001.All scale bar: 20 μm.

FIGS. 5A-5V show fatty acid uptake contributes to cisplatin resistanceby increasing fatty acid oxidation. FIGS. 5A-5C are oxygen consumptioncurves for OVCAR5-cisR and OVCAR5 over 3 hours (FIG. 5A), andOVCAR5-cisR (FIG. 5B) and OVCAR5 (FIG. 5C) with 40 μM etomoxir treatmentover 3 hours; n=4 biological replicates. FIG. 5D shows quantification ofoxygen consumption rate (OCR) for OVCAR5 and OVCAR5-cisR cells treatedwith (n=4) or without (n=6) etomoxir (40 μM) measured by using theextracellular oxygen consumption kit (Abcam); P=0.00044 and 0.041. FIG.5E is an OCR profile measured with an Seahorse® XF Analyzer (SeahorseBioscience/Agilent)(Seahorse®) of OVCAR5 and OVCAR5-cisR cells with orwithout etomoxir treatment, followed by injections of mitochondrialrespiration inhibitors oligomycin,carbonylcyanide-p-trifluoromethoxyphenylhydrazone (FCCP), rotenone, andantimycin A (indicated by arrows); n=3 biological replicates. FIG. 5Fshows quantified etomoxir induced basal respiration, ATP production, andmaximal respiration reduction in OVCAR5 and OVCAR5-cisR cell; data arepresented as mean+SD; n=3 technical replicates; two-sided Student's ttest; P=0.0021, 0.018 and 0.0046; * P<0.05 and ** P<0.01. FIG. 5G showsquantification of OCR for PEO1 and PEO4 cells measured through Seahorse®XF Analyzers; n=6 technical replicates. P=8.9×10⁻⁵. FIGS. 5H-5J showdose-response to etomoxir for cisplatin resistant cell lines and theirparental cell lines including PEO1 and PEO4 (FIG. 5H), OVCAR5 andOVCAR5-cisR (FIG. 5I), and COV362 and COV362-cisR (FIG. 5J). FIGS. 5K-5Mshow dose-response to cisplatin with or without supplemental etomoxirtreatment at 40 μM for PEO4 (FIG. 5K), OVCAR5-cisR (FIG. 5L), andCOV362-cisR (FIG. 5M) cells. FIG. 5N presents relative mRNA expressionlevels of CPT1a in OVCAR5-cisR shCtrl and shRNA targeting CPT1a(shCPT1a) cell; n=3. P=0.033. FIG. 5O is a western blot of CPT1a andGAPDH for OVCAR5-cisR cells transduced with shCtrl and shCPT1a cell; n=3biological replicates. FIG. 5P is a dose-response to cisplatin forOVCAR5-cisR shCtrl and shCPT1a cell. The data in all the does-responsecurves FIGS. 5A-5C, 5E, 5H-5M, and 5P are shown as means±SD; n=3. FIG.5Q shows relative mRNA expression levels for CPT1a in OVCAR5 andOVCAR5-cisR cells; n=4. FIG. 5R is a western blot for CPT1a and GAPDH inOVCAR5 (n=2) and OVCAR5-cisR cells (n=3). The results in bar chartsFIGS. 5D, 5G, 5N, 5Q are shown as means+SD. For FIGS. 5D, 5G, 5N-50, and5Q-5R, statistical significance was analyzed using one-sided Student's ttest; * P<0.05. *** P<0.001. n.s. P>0.05. FIGS. 5S and 5T are heatmapcharts of lipid metabolism related genes, as analyzed by RNA-sequencingin OVCAR5 (FIG. 5S) and SKOV3 (FIG. 5T) cell line pair; fatty acidoxidation (FAO) related genes are highlighted in red, and lipogenesisrelated genes are depicted in green. FIG. 5U is a total tumor volumegrowth curve from day 14 to 37 after tumor cell inoculation for vehicle(n=3), carboplatin (n=3), etomoxir (n=4) and combinational (n=6)treatment groups. FIG. 5V shows mice body weight record since tumorinoculation for vehicle (n=3), carboplatin (n=3), etomoxir (n=4) andcombinational (n=6) treatment groups; data for PDX in vivo experiment inFIGS. 5U-5V are shown as means±SEM.

FIGS. 6A-6Q illustrate increased fatty acid uptake and oxidationsupports cancer cell survival under cisplatin-induced oxidative stress.FIG. 6A shows representative bright field and fluorescent images ofOVCAR5 (n=55) and OVCAR5-cisR (n=49) cells after treated with afluorescent probe 2′,7′-Dichlorofluorescin diacetate (DCFDA) cellularreactive oxidative species (ROS) assay kit. FIG. 6B presentsquantification of DCF fluorescent signal intensity for FIG. 6A;P=4.6×10⁻²⁹. FIG. 6C shows representative bright field and fluorescentimages of PEO1 and PEO4 cells using the DCFDA cellular ROS assay kit;scale bar: 30 μm. FIG. 6D shows quantification of DCF fluorescent signalintensity for PEO1 and PEO4 cells; bound of outer box indicates 25% to75% of data; inner box indicates mean; lines represent medium; whiskersindicate SD; circles indicate maxima and minima of data; n=10;P=0.00013. FIG. 6E presents quantification of DCF fluorescent signalintensity of OVCAR5 and OVCAR5-cisR cells with cisplatin treatment at1.6 μM or 3.3 μM for 24 hours; n=2. P=0.0064, 0.040 and 0.016. FIGS.6F-6G show quantified NADPH/NADP ratio of PEO1 and PEO4 (FIG. 6F), andOVCAR5 and OVCAR5-cisR (FIG. 6G); n=3. P=2.4 ×10⁻⁵ and 0.048. FIG. 6Hillustrates extracellular acidification rate (ECAR) profile of PEO1 andPEO4 cells after treatment with 13.2 μM cisplatin measured by Seahorse®;n=5; P=0.041; means±SD. FIG. 6I shows quantification of PEO1 and PEO4cells' ECAR before and 30 minutes after 13.2 μM cisplatin treatment;n=5. FIG. 6J shows OCR profiles of PEO1 and PEO4 after 13.2 μM cisplatintreatment measured by Seahorse®; means±SD; n=5. FIG. 6K showsquantification of OCR for PEO1 and PEO4 cells before and 30 minutesafter treatment with 13.2 μM cisplatin measured by Seahorse®; means+SD;n=5. FIG. 6L shows representative bright field and fluorescent images ofOVCAR5 and OVCAR5-cisR cells with 100 μM fluorescent glucose analog2-NBDG treatment for 2 hours after incubation with 3.3 μM cisplatin for24 hours. FIG. 6M shows quantified fluorescent signal intensity for FIG.6L; n=13, 16, 16 and 17. P=0.019 and 8.2×10⁻⁷. FIGS. 6N-6O showquantified ATP/ADP ratio of cisplatin resistant cell lines and theirparental cell lines involving OVCAR5 (n=3) and OVCAR5-cisR (n=2) (FIG.6N), and PEO1 (n=5) and PEO4 (n=6) (FIG. 6O); n represents biologicalreplicates. P=0.0071 and 0.039. FIG. 6P shows quantified ATP/ADP ratioof OVCAR5 and - OVCAR5-cisR treated with 3.3 μM cisplatin with orwithout supplement of 100 μM palmitic acid for 6 hours; n=3 biologicalreplicates. P=0.046. FIG. 6Q illustrates the proposed mechanism forcisplatin effect on cellular metabolism and cell proliferation. AllScale bar: 30 μm. For box plots (FIGS. 6B and 6M), the bound of outerbox indicates 25% to 75% of data; inner box indicates mean; linesrepresent medium; whiskers indicate SD; circles indicate maxima andminima of data. Data in bar charts FIGS. 6E-6G and 6N-6P are shown asmeans+SD. All statistical significance was analyzed using one-sidedStudent's t test. * P<0.05, ** P<0.01, and *** P<0.001.

FIGS. 7A-7L show that cisplatin induced fatty acid uptake is a universalmetabolic feature in multiple types of cancers. FIGS. 7A-7C showdose-response to cisplatin for Mia Paca2 cells (FIG. 7A), A549 cells(FIG. 7B), and MD-MBA231 cells (FIG. 7C). The data are shown asmeans±SD; n=3. FIG. 7D presents representative bright field and SRSimages of Mia Paca2 cells treated with 6.6 μM cisplatin for 24 hoursfollowed by 100 μM PA-d₃₁ or OA-d₃₄ incubation for 6 h. FIG. 7E showsquantitation of C-D signal in Mia Paca-2 cells treated with or withoutcisplatin by mean intensity; n=7 for PA-d31 and n=8 for OA-d34.P=0.00029 and 0.026. FIG. 7F shows quantitation of C-D signal in MiaPaca-2 cells treated with or without cisplatin by fold of change; (n=7for PA-d31 and n=8 for OA-d34). FIG. 7G shows representative brightfield and SRS images of A549 cells treated with 13.2 μM cisplatin for 48hours followed by 100 μM PA-d₃₁ or OA-d₃₄ incubation for 6 h. FIG. 7Hshows quantitation of C-D signal in A549 cells treated with or withoutcisplatin by mean intensity; n=8. P=0.0031 and 0.016. FIG. 7I showsquantitation of C-D signal in A549 cells treated with or withoutcisplatin by fold of change; (n=8). FIG. 7J presents representativebright field and SRS images of MDA-MB-231 cells treated with 6.6 μMcisplatin for 24 hours followed by 100 μM PA-d₃₁ or OA-d₃₄ incubationfor 6 h. FIG. 7K shows quantitation of C-D signal in MDA-MB-231 cellstreated with or without cisplatin by mean intensity; n=6. P=0.0024. FIG.7L shows quantitation of C-D signal in MDA-MB-231 cells treated with orwithout cisplatin by fold of change; (n=6). Data in all bar charts(FIGS. 7E, 7H, 7K) are shown as means+SD. All statistical significancewas analyzed using one-sided Student's t test. P=0.00029, 0.026, 0.0031,0.016 and 0.0024;* P<0.05. ** P<0.01. *** P<0.001. All scale bar: 20 μm.

FIG. 8 illustrates cellular metabolism reprogramming from glycolysis tofatty acid oxidation in cisplatin-resistant an ovarian cancer cell.

DETAILED DESCRIPTION

It is to be understood that the descriptive embodiments in thisdisclosure are not limited to particular methods, reagents, compounds,compositions or biological systems, which can, of course, vary. It isalso to be understood that the terminology used herein is for thepurpose of describing particular embodiments only, and is not intendedto be limiting.

Definitions

Unless defined otherwise, all technical and scientific terms used inthis disclosure with the appended claims have the same meaning that iscommonly understood by one of ordinary skill in art to which the subjectmatter pertains. As used in this disclosure and the appended claims,unless specified to the contrary, the following definitions are setforth to provide meaning and scope of the terms and to facilitate theunderstanding of the disclosure.

The terms “a,” “an,” “the” and similar references used in the context ofthe present disclosure include both the singular and the plural, unlessindicated or clearly contradicted by context. All methods describedherein can be performed in any suitable order unless otherwise indicatedor clearly contradicted by context otherwise. The use of any and allexamples, or exemplary language (e.g., “such as”) is intended merely toilluminate the disclosure and does not pose a limitation on the scope ofthe invention otherwise claimed.

Furthermore, the term “about,” as used herein when referring to ameasurable value such as an amount, dose, time, temperature, and thelike, is meant to encompass variations of±20%, ±10%, ±5%, ±1%, ±0.5%, oreven±0.1% of the specified amount.

Also as used herein, “and/or” refers to and encompasses any and allpossible combinations of one or more of the associated listed items, aswell as the lack of combinations when interpreted in the alternative(“or”).

As used herein the term “comprising” or “comprises” is used in referenceto compositions, methods, and respective component(s) thereof, that areessential to the method or composition, yet open to the inclusion ofunspecified elements, whether essential or not.

The term “consisting of” refers to compositions, methods, and respectivecomponents thereof as described herein, which are exclusive of anyelement not recited in that description of the embodiment.

As used herein the term “consisting essentially of” refers to thoseelements required for a given embodiment. The term permits the presenceof additional elements that do not materially affect the basic and novelor functional characteristic(s) of that embodiment of the invention.

As used herein, the terms “treat”, “treatment”, and “treating” refer toadministering a compound, composition, agent, therapeutic, or apharmaceutical composition containing the same for therapeutic purposes.As used herein, the terms “compound,” “composition,” “agent,”“therapeutic,” or “drug” used or useful for treatment may be usedinterchangeably.

As used herein, the term “cancer” refers to an abnormal cell thatdivides without control and can spread or invade into a tissue or spreadthroughout the body, and a disease or condition with such abnormalcells. The term “cancer” as used herein may be used interchangeably with“tumor,” “malignancy,” and “malignant neoplasm.”

As used herein, the term “benign neoplastic cell” refers to an abnormalcell that divides more than normal, and does not spread or invade into atissue, and “benign neoplasm” refers to a collection or mass of abnormalcells that divide more than normal. A benign neoplasm is not cancer.

As used herein, the term “immune cell” refers to a cell that is part ofthe immune system and helps the body fight infection or other disease orcondition.

Detailed Description

This disclosure provides an assay for determining resistance in a targetcell or tissue to a therapy associated with cellular stress orperturbation, an assay for determining metabolic reprogramming in atarget cell or tissue to a therapy associated with cellular stress orperturbation, and methods of using the assays.

1. Assay for determining resistance in a target cell or tissue totherapy.

Increased aerobic glycolysis is widely considered as a hallmark ofcancer. A metabolic reprograming in a cancer cell is known to occurduring development of therapeutic resistance. Yet, the mechanism ofcellular metabolic reprograming during development of therapeuticresistance to stress and inhibition of aerobic glycolysis is unknown. Asdisclosed through chemical microscopy or spectroscopy, cells resistantto therapy induced cellular stress are found to exhibit increased uptakeof exogenous fatty acids (FAs), accompanied with decreased glucoseuptake and de novo lipogenesis. The alteration or change to increaseduptake of exogenous fatty acids accompanied with decreased glucoseuptake and de novo lipogenesis is an indication of reprogramming fromglucose and glycolysis-dependent anabolic and energy metabolism to fattyacid uptake and beta-oxidation dependent anabolic and energy metabolism.Mechanistically, the increased fatty acid uptake facilitates cellsurvival under therapy induced cellular stress by enhancing energyproduction through beta-oxidation.

One aspect of the disclosure is an assay for determining resistance in atarget cell or tissue to a therapy associated with cellular stress thatincludes measuring with chemical microscopy or spectroscopy a functionalmetabolic change in the target cell or tissue, and determining ametabolic index of resistance in the target cell to the therapy. Thefunctional metabolic change is a switch or change from glucose andglycolysis dependent anabolism and energy metabolism to fatty aciduptake and fatty acid oxidation dependent anabolism and energymetabolism. In embodiments, the metabolic index provides a level ofresistance in a target cell to a therapy.

A metabolic index incorporates a measurement of glucose derivedanabolism and fatty acid uptake and oxidation. The metabolic index is aratio of an increase in fatty acid uptake and oxidation to a decrease inglucose-dependent anabolism in a target cell, such as a cancer cell. Asexemplified, the ratio of fatty acid uptake to glucose derived anabolismis defined as the “metabolic index.” The ratio may provide adimensionless number ranging from 0 to 1, as an index of the generalformula fatty acid uptake/(fatty acid uptake+glucose derived anabolism).For example, resistance to cisplatin in cancer cells quantitativelydetermined by a metabolic index was determined with deuterium labeledpalmitic acid-d₃₁ (PA-d₃₁) and deuterium labeled glucose-d₇ in variouscell lines, PA-d₃₁/(PA-d₃₁+Glucose-d₇). The index linearly correlated tothe IC₅₀ to cisplatin (see, FIG. 3C). Similar metabolic indices weredetermined from increase of bond signal in various cells (Example 3,FIGS. 3D-3J).

In the embodiments herein, the metabolic index correlates to a level ofresistance to the therapy in a target cell or tissue. In embodiments,the metabolic index correlates to resistance to a therapy in the targetcell or tissue when a metabolic change is a decrease in glucose andglycolysis dependent anabolism and an increase in fatty acid uptake andfatty acid oxidation dependent anabolism and energy metabolism. In someembodiments, the metabolic index correlates linearly to a level ofresistance to the therapy in the target cell or tissue.

In the embodiments herein, the target cell is a cell that may undergometabolic reprogramming or alteration in response to cellular stress,such as a cancer cell, an immune cell, or a benign neoplastic cell. Inthe embodiments, the target cell is a cancer cell from any cancer, forexample ovarian, prostate, testicular, bladder, pancreatic, lung,breast, esophageal, head, or neck cancer.

Fatty acid oxidation is as an alternative path for energy production,and has been shown to be upregulated in certain conditions, such asunder metabolic stress [59]. However, it is less known whether and howfatty acid oxidation is deregulated in drug resistant cells, such ascancer cells. As disclosed herein, cellular stress leads to increasedfatty acid uptake and oxidation. For example, oxidative stress depletesintracellular antioxidants, such as NADPH, and thus suppresses de novolipogenesis that requires such antioxidants. Decreased de novolipogenesis could result in a lower level of malonyl-CoA, which is anallosteric inhibitor of CPT1, and may trigger higher activity of CPT1[60, 61]. The decreased de novo lipogenesis is at least partiallyresponsible for the observed increased in fatty acid activity. In theembodiments, the metabolic index further correlates to resistance to thetherapy associated with cellular stress in the target cell or tissuewhen the metabolic change is a decrease in de novo lipogenesis in thetarget cell or tissue.

In embodiments, measuring with chemical microscopy functional metabolicchange comprises measuring glucose and glycolysis derived anabolism in atarget cell, measuring fatty acid uptake and oxidation in the targetcell, and determining a change from glucose anabolism in the target cellto fatty acid uptake and oxidation energy metabolism. In someembodiments, measuring with chemical microscopy functional metabolicchange further comprises measuring de novo lipogenesis in the targetcell, and determining a change from glucose and glycolysis dependentanabolism and de novo lipogenesis in the target cell to fatty aciduptake and oxidation energy metabolism.

Another embodiment is an assay for determining resistance in a targetcell or tissue resistant to therapy associated with cellular stress orperturbation that includes measuring functional metabolic change in atarget cell by measuring with chemical microscopy glucose-derivedanabolism in the target cell and fatty acid uptake in the target cell,and determining a ratio of the fatty acid uptake to the glucoseanabolism in the target cell to provide a metabolic index of resistancefor the target cell. In embodiments, measuring with chemical microscopyfunctional metabolic change in a target cell comprises measuring withchemical microscopy glucose-derived anabolism in the target cell andmeasuring with the chemical microscopy fatty acid uptake in the targetcell, in which glucose-derived anabolism, and optionally de novolipogenesis, are decreased and fatty acid uptake is increased. In someembodiments, the metabolic index correlates to a level of resistance toa therapy in the target cell. In some embodiments, the correlation islinear.

In the embodiments herein, the chemical microscopy is any microscopy orspectroscopy that provides for single cell analysis. In the embodiments,the chemical microscopy is Raman scattering microscopy or infraredmicroscopy. In some embodiments, the Raman scattering microscopy may bespontaneous Raman scattering microscopy, surface enhanced Ramanscattering microscopy, or coherent Raman scattering microscopy. CoherentRaman scattering microscopy may be coherent anti-stokes Raman scattering(CARS) or stimulated Raman scattering (SRS) microscopy. SRS microscopy,for example, is a label-free chemical imaging technique that detects theintrinsic chemical bond vibrations. To identify the altered lipidmetabolism in resistant cells, a high-throughput single-cell analysisapproach was used with large-area hyperspectral SRS scanning (see,[39]). A stack of large-area hyperspectral SRS images was obtained,containing hundreds of individual cells in each field-of-view. (FIG. 1A)An SRS spectrum was extracted at each pixel from the image stack. Then,the hyperspectral SRS images were segmented through a spectral phasoralgorithm to generate maps of intracellular compartments correspondingto nuclei and lipids based on the spectrum similarity. The nuclei mapwas inputted into CellProfiler™ to guide the identification of the edgesof each individual cell from the raw whole cell image. After individualcells were outlined, the lipid map was mapped back to the correspondingcells. Finally, quantitative characterization of lipids in terms ofintegrated intensity, mean intensity, area, and lipid droplet size ineach individual cell were performed. Thus, the intrinsic chemical bondvibrations provide indication of altered cell metabolism, for examplelipid metabolism, in resistant cells. In some embodiments, the chemicalmicroscopy used in the assay comprises hyperspectral stimulated Ramanscattering imaging. In some embodiments, the chemical microscopycomprises hyperspectral stimulated Raman scattering imaging to providehigh-throughput vibrational imaging.

In the embodiments herein, infrared microscopy may be mid-infraredphotothermal (MIP) microscopy or direct infrared absorption basedmicroscopy, such as fourier-transformed infrared (FTIR) microscopy orquantum cascade laser (QCL) microscopy. In some embodiments, theinfrared microscopy is MIP microscopy.

In the embodiments herein, cellular stress or perturbation may originatefrom a variety of sources that cause a shift in cellular conditions. Insome embodiments, cellular stress is oxidative stress, metabolic stress,hypoxic stress, nutrient stress, thermal stress, genotoxic stress, orcombinations thereof. In embodiments, the therapy induces cellularstress in the target cell or tissue.

In the embodiments herein, the therapy induces metabolic reprogrammingor metabolic alteration or change in a cell. In some embodiments, thetherapy is cancer therapy. In the embodiments, cancer therapy isselected from chemotherapy, radiotherapy, immunotherapy, targetedtherapy, hormone therapy, light or laser therapy, photodynamic therapy,and combinations thereof. In the embodiments, the cancer therapy ischemotherapy, such as alkylating agent, for example platinum-basedagents and nitrosoureas, anti-metabolite, anti-tumor antibiotic, plantalkaloid, for example topoisomerase inhibitors and mitotic inhibitorshormonal agent such as corticosteroids, and biological responsemodifier. Platinum-based drugs, such as carboplatin and oxaliplatin, arewidely used chemotherapy agents for multiple types of cancers, includingovarian, testicular, bladder, head and neck, non-small-cell lung cancerand others. Despite the high response rate following initial treatment,the effects of platinum-based drugs such as cisplatin and carboplatinare limited by severe side effects and high probability of drugresistance development [53, 54]. For example, the majormechanism-of-action of cisplatin is formation of DNA-adducts, whichblock transcription and DNA synthesis, while at the same time, activateDNA damage response mechanisms and mitochondrial detoxificationmechanisms. Apoptosis eventually ensues if DNA lesions are not repaired,and oxidative stress is not buffered. Numerous efforts have been devotedto elucidating the mechanisms of cancer cell resistance to cisplatin.Most of these studies focused on adduct formation and subsequentactivation of cell death pathways, for example, reduced formation ofDNA-adducts due to altered uptake/efflux, enhanced DNA damage repair, orimpaired mitochondrial apoptosis pathway after adduct formation [54].Other mechanisms of cisplatin resistance have received much lessattention. Studies have shown that cisplatin can have anothermechanism-of-action by inducing oxidative stress in ovarian [47, 55],prostate [56], and lung cancer [57]. A few studies highlighted anassociation between metabolic reprogramming and cisplatin resistance.Alterations in glycolysis pathway were associated with cisplatingenerated oxidative stress in head and neck squamous cell carcinoma[58]. Lipid droplet production mediated by lysophosphatidylcholineacyltransferase 2 is linked to resistance to oxaliplatin in colorectalcancer [24]. Adipocyte induced FABP4 upregulation was found to mediatecarboplatin resistance in ovarian cancer [44]. As disclosed herein, ametabolic alteration or switch is found to occur from glucose dependentanabolic and energy metabolism to fatty acid uptake and fatty acidoxidation in chemotherapy resistant cancer cells, to adapt tochemotherapy-induced oxidative stress.

One embodiment is an assay for determining resistance in a cancer cellresistant to therapy associated with cellular stress that includesmeasuring with chemical microscopy or spectroscopy a functionalmetabolic change in the target cell or tissue, and determining ametabolic index of resistance in the target cell to the therapy. Inembodiments, measuring with chemical microscopy a functional metabolicchange in the target cell or tissue includes measuring with chemicalmicroscopy glucose-derived anabolism in the cancer cell, measuring withthe chemical microscopy fatty acid uptake in the cancer cell, anddetermining a ratio of the fatty acid uptake to the glucose anabolism inthe cancer cell to provide a metabolic index of resistance for thecancer cell. In some embodiments, the metabolic index correlates to alevel of resistance to a therapy in the cancer cell. In someembodiments, the cancer cell is selected from ovarian, prostate,testicular, bladder, pancreatic, lung, breast, esophageal, head, andneck cancer. In some embodiments, the cellular stress is oxidativestress. In some embodiments, the therapy is cancer therapy. Inembodiments, the cancer therapy induces a metabolic alteration in thecell.

In some embodiments herein, the cancer therapy is chemotherapy. In someembodiments, the chemotherapy is a platinum-based agent or therapeutic.In some embodiments, the platinum-based therapy or therapeutic isselected from cisplatin, carboplatin, oxaliplatin, and nedaplatin, andcombinations thereof.

Besides fatty acid oxidation, the process of fatty acid uptakerepresents a target for overcoming drug resistance. The regulation offatty acid uptake involves multiple and redundant transporters, bindingproteins and carrier proteins [42, 43, 45, 62], and fatty acid uptakecontributes to several mechanisms significant for tumor survival andgrowth, including membrane biogenesis, fatty acid pool replenishment,and ER stress prevention [63, 64]. Aside from pointing towards apotential therapeutic strategy for therapy-resistant cancers, ex vivoquantitative metabolic imaging of anaerobic glycolysis, de novolipogenesis and fatty acid uptake in tumor cells represent a newfunctional marker for therapy responsiveness in clinical specimens atthe single cell level. The conventional methods to determine celltherapy resistance rely on cell viability assays or measurement ofvarious protein markers, which are time-consuming and lack accuracy. Thedisclosed metabolic imaging approach provides a fast, functional, andquantitative way to determine target cell or tissue resistance based onfunctional metabolic signatures in resistant cells.

2. Methods of Inhibiting Resistance in a Target Cell to Therapy

Another aspect of the disclosure is use of the disclosed assays inmethods of treating or inhibiting resistance in a target cell or tissueto a therapy associated with cellular stress or perturbation.Embodiments include a method of treating or inhibiting resistance in atarget cell or tissue to a therapy associated with cellular stress in asubject by performing an assay as disclosed herein to determine ametabolic index of resistance in a target cell or tissue to the therapy,administering at least one inhibitor of fatty acid oxidation to thesubject, and administering at least one therapy to the subject. Inembodiments, measuring with chemical microscopy a functional change inmetabolism comprises measuring glucose and glycolysis dependentanabolism in the target cell, measuring fatty acid uptake oxidation inthe target cell, in which glucose and glycolysis dependent anabolism isdecreased and fatty acid uptake and oxidation is increased. Inembodiments, the metabolic index of resistance in the target cell to thetherapy further includes a decrease in de novo lipogenesis.

In embodiments, the metabolic index is a ratio of an increase in fattyacid uptake and oxidation to a decrease in glucose-dependent anabolismin a target cell. An embodiment is a method of treating or inhibitingresistance in a target cell or tissue to a therapy associated withcellular stress comprising measuring with chemical microscopy afunctional change in metabolism from glucose and glycolysis dependentanabolism to fatty acid uptake in which glucose and glycolysis dependentanabolism is decreased and fatty acid uptake and oxidation is increased,and determining a metabolic index of resistance in the target cell ortissue to a therapy, administering at least one inhibitor of fatty acidoxidation, and administering at least one therapy. In embodiments, themetabolic index correlates to resistance to the therapy in the targetcell or tissue when the functional metabolic change is a decrease inglucose and glycolysis dependent anabolism and an increase in fatty aciduptake and fatty acid oxidation dependent anabolism and energymetabolism. In embodiments, the metabolic index of resistance in thetarget cell to the therapy further includes a decrease in de novolipogenesis.

Another embodiment is a method of treating or inhibiting resistance in atarget cell or tissue to a therapy associated with cellular stress in asubject comprising measuring with chemical microscopy functional changein metabolism in a target cell or tissue from glucose and glycolysisdependent anabolism to fatty acid uptake in which glucose and glycolysisdependent anabolism is decreased and fatty acid uptake and oxidation isincreased, and determining a metabolic index or ratio of a decrease inglucose and glycolysis dependent anabolism and an increase in fatty aciduptake and oxidation indicating resistance in the target cell or tissueto a therapy, administering at least one inhibitor of fatty acidoxidation to the subject, and administering at least one therapy to thesubject.

In embodiments, measuring with chemical microscopy functional metabolicchange comprises measuring with the chemical microscopy glucose andglycolysis derived anabolism in the target cell, measuring with thechemical microscopy fatty acid uptake and oxidation in the target cell,and determining a change from glucose and glycolysis derived anabolismin the target cell to fatty acid uptake and oxidation energy metabolism.

A further embodiment is a method of treating or inhibiting resistance ina cancer cell to therapy associated with cellular stress in a subjectcomprising measuring with chemical microscopy glucose-derived anabolismin the cancer cell, measuring with the chemical microscopy fatty aciduptake in the cancer cell, and determining a ratio of the fatty aciduptake to the glucose anabolism in the cancer cell to obtain a metabolicindex, administering an inhibitor of fatty acid oxidation to thesubject, and administering a therapy to the subject. In the embodiments,the administering may be in any form effective for the treatment.

In some embodiments, the metabolic index correlates to a level ofresistance in the cancer cell to a therapy. In some embodiments, thecorrelation is linear.

In the embodiments, the method further comprises obtaining a cancer cellfrom the subject to perform the assay. The subject may be any mammal,for example human. The cancer cell can be from any cancer. In theembodiments, the cancer is selected from ovarian, prostate, testicular,bladder, pancreatic, lung, breast, esophageal, head, and neck cancer.

In the embodiments herein, the therapy induces metabolic alteration orchange in a cell. In some embodiments, the therapy is cancer therapy. Inthe embodiments, cancer therapy is selected from chemotherapy,radiotherapy, immunotherapy, targeted therapy, hormone therapy, light orlaser therapy, photodynamic therapy, and combinations thereof. In someembodiments, the cancer therapy is chemotherapy selected from analkylating agent, for example platintim-based agents and nitrosoureas,an anti-metabolite, an anti-tumor antibiotic, a plant alkaloid, forexample topoisomerase inhibitors and mitotic inhibitors, a hormonalagent such as corticosteroids, a biological response modifier, andcombinations thereof. In some embodiments, the chemotherapy is aplatinum-based agent or therapeutic, selected from cisplatin,carboplatin, oxaliplatin, nedaplatin, and combinations thereof. In theembodiments, the administering may be in any form effective for thetreatment.

In embodiments, fatty acid oxidation is inhibited in the cancer cell andthe therapy induces cellular stress in the cancer cell, therebyinhibiting resistance to the therapy. In embodiments, the at least oneinhibitor of fatty acid oxidation is a small molecule inhibitor or agenetic perturbation (e.g., gene deletion, gene overexpression,insertion mutation), or a combination thereof. In embodiments, theinhibitor of fatty acid oxidation is selected from etomoxir, oxfenicine,perhexiline, mildronate, trimetazidine, and combinations thereof.

Collectively, a new means for rapid detection of resistance to therapyat a single cell level, and a new strategy for treating tumors resistantto therapy are disclosed. Through large-area chemical microscopy imagingand subsequent single-cell analysis, a stable, metabolic change orswitch is shown from glucose and glycolysis dependent anabolic andenergy metabolism to fatty acid uptake and fatty acid beta-oxidationdependent anabolic and energy metabolism. By coupling metabolic fluxthrough isotope labeling and microscopic molecular imaging, resistantcells display increased uptake of exogenous fatty acid, accompanied withdecreased glucose uptake and de novo lipogenesis. By incorporatingmicroscopic imaging-based measurements of glucose derived anabolism andfatty acid uptake, a “metabolic index” may be determined, defined as theratio of fatty acid uptake versus glucose incorporation. The metabolicindex correlates to the level of resistance to therapy in target cells,such cancer cells, and in primary human cells. This correlationdemonstrates the potential of using microscopy or spectroscopy imagingfor rapid detection of resistance to therapy in cancer cells ex vivo.

Mechanistically, resistant cells display higher fatty acid oxidationrate, which supplies additional energy and promotes cell survival undercellular stress. Blocking fatty acid oxidation by a small moleculeinhibitor or genetic perturbation in combination therapy, for exampleplatinum-based treatment, synergistically suppresses cell proliferationin vitro and growth of a patient-derived xenograft model in vivo. Thisfurther provides new treatment options for patients with tumorsresistant to therapy such as platinum-based therapy, for examplecisplatin-resistant cells, by targeting the fatty acid oxidationpathway.

The described technology is further illustrated by the followingexamples which in no way should be construed as being further limiting.

EXAMPLES

Materials and Methods

The described research complied with all relevant ethical regulations.Animal studies were approved by the Institutional Animal Care and UseCommittee (IACUC) at Northwestern University and were performed in theDevelopmental Therapeutics Core (DTC) of the Lurie Cancer Center.

Glucose-d₇, palmitic acid-d₃₁ (PA-d₃₁), and oleic acid-d₃₄ (OA-d₃₄) werepurchased from Cambridge Isotope Laboratory. 17-Octadecynoic Acid(ODYA), BMS309403, cisplatin, and etomoxir were purchased from CaymanChemicals. For treatment with cisplatin, 3.3 μM was used as the finalconcentration, unless otherwise specified.

Cell Lines

Ovarian cancer cell lines used in the methods include SKOV3, PEO1,OVCAR5, and COV362, and their cisplatin-resistant counterparts includeSKOV3-cisR, PEO4, OVCAR5-cisR, and COV362-cisR. SKOV3 (Cat#: HTB-77),Mia Paca2 (Cat#: CRL-1420), MDA-MB-231 (Cat#: CRM-HTB-26) and A549(Cat#: CCL-185) cells were purchased from the American Type CultureCollection (ATCC, Manassas, Va.). PEO1 (Cat#: 10032308) and PEO4 (Cat#:10032309) were purchased from Sigma Aldrich. OVCAR5 cells were agenerous gift from Dr. Marcus Peter, Northwestern University, and COV362cells were from Dr. Kenneth Nephew, Indiana University. All cell lineswere authenticated and tested to be mycoplasma negative. Mia Paca2 andA549 are in the list of known misidentified cell lines maintained by theInternational Cell Line Authentication Committee, while theirauthentication was performed by ATCC through STR profiling. Theresistant cell lines SKOV3-cisR, COV362-cisR, and OVCAR5-cisR weregenerated by treatment with 3 or 4 repeated or increasing doses ofcisplatin for 24 hours. Surviving cells were allowed to recover for 3 to4 weeks before receiving the next treatment. Changes in resistance toplatinum were estimated by calculating half maximal inhibitoryconcentration (IC₅₀) values [40]. PEO1, PEO4, OVCAR5, and OVCAR5-cisRcells were cultured in RPMI 1640 medium supplemented with 2 mML-glutamine, 10% FBS and 100 units/mL penicillin/streptomycin. SKOV3,SKOV3-cisR, COV362, and COV362-cisR and Mia Paca2 cells were cultured inhigh-glucose DMEM medium supplemented with 10% FBS and 100 units/mLpenicillin/streptomycin. MDA-MB-231 and A549 cells were cultured inLeibovitz's L-15 medium and Kaighn's Modification of Ham's F-12 Mediumrespectively supplemented with 10% FBS and 100 units/mLpenicillin/streptomycin. For CPT1a knockdown cell lines development,cells were transfected with CPT1a or control shRNA lentiviral particles(Sigma Aldrich) Sigma Aldrich, TRCN0000036282) for 48 hours and selectedby 1 μg/ml puromycin for one week. All cells were cultured at 37° C. ina humidified incubator with 5% CO₂ supply.

Primary Human Cells

De-identified high grade serous ovarian tumors (HGSOC) and malignantascites fluid specimens from ovarian cancer (OC) patients were obtainedat the time of cytoreductive surgery either upfront or after neoadjuvantchemotherapy (interval debulking surgery) at the Northwestern UniversitySchool of Medicine under an IRB approved protocol (STU00202468). Allpatients were followed prospectively and received platinum and taxanestandard of care chemotherapy. Platinum resistance was defined asdisease recurring within 6 months from completing carboplatin-basedchemotherapy, as assessed clinically, by CA125 criteria or CT scans.Tumor tissues were enzymatically disassociated into single cellsuspensions and cultured as previously described [65, 66]. Aftercentrifugation at 200 g for 5 min, 25,000 ascites derived tumor cellswere cultured as monolayers in DMEM medium supplemented with 10% FBS andantibiotics prior to stimulated Raman scattering (SRS) imaging.

In Vivo Experiments

The platinum resistant PDX model was developed as previously described[67]. After passage through a donor animal, fresh tumor (equal size) wasimplanted subcutaneously (SC) in 20 female 7-8-week-old NSG mice(Jackson Labs, Cat#: JAX:00555). Tumor sizes were measured usingcalipers twice per week and tumor volumes were calculated according tothe formula length×width²/2. When the tumor volume reached 100 mm³, theanimals were randomized into four groups: vehicle, carboplatin alone (10mg/kg weekly intraperitoneal (i.p.) injection), etomoxir alone (40 mg/kgdaily i.p. injection), and combination of carboplatin (10 mg/kg weeklyi.p. injection) and etomoxir (40 mg/kg daily i.p. injection). Bodyweights and habitus were monitored twice per week and mice weresacrificed when the largest tumor exceeded 1500 mm³ or if humanendpoints were reached earlier.

Xenografts were obtained through intraperitoneally implantation of 2million OVCAR5 cells in female (6-8 weeks old) athymic nude mice(Foxn1^(nu), Envigo). Two weeks after inoculation, tumor harboring micewere treated with PBS (sensitive group, n=3) or 25 mg/kg carboplatin(resistant group, n=3) via weekly i.p. injection for three cycles.Tumors were collected and weighted one week after the last cycle andfrozen. For SRS imaging, tumors were sectioned at 5-10 nm thicknessslices by cryostat. To isolate tumor cells from tissue, PBS andcarboplatin treated xenografts were mechanically and enzymaticallydissociated in Dulbecco's modified Eagle's medium/F12 (Thermo FisherScientific) containing collagenase (300 IU/ml, Sigma-Aldrich) andhyaluronidase (300 IU/ml, Sigma-Aldrich) for 2-4 hours at 37° C. Redblood cell lysis used RBC lysis buffer (BioLegend), followed by DNase(Qiagen) treatment and filtering through a 40 μm cell strainer (FisherScientific) to yield single cells suspension, which were examined forresponsiveness to cisplatin ex vivo.

For all animal experiments, mice were housed at 21° C. −23° C. with a12/12 dark/light cycle. The humidity of housing environment is 35%. Micewere sacrificed when the largest tumor exceeded 1500 mm³ or if humanendpoints were reached earlier. The mouse diet was Cat# is 7912 fromTeklad/Envigo.

Large-Area Hyperspectral Stimulated Raman Scattering Imaging

Hyperspectral SRS imaging was performed on a lab-built system followingpreviously published method [8]. The laser source was a femtosecondlaser (InSight™ DeepSee™, Spectra-Physics™, Santa Clara, Calif., USA)operating at 80 MHz with two synchronized output beams, a tunable pumpbeam ranging from 680 nm to 1300 nm, and a Stokes beam fixed at 1040 nm.For imaging at the C-H vibration region (2800˜3050 cm⁻¹), pump beam wastuned to 798 nm. The Stokes beam was modulated at 2.3 MHz by anacousto-optic modulator (1205-C, Isomet®). After combination, both beamswere chirped by two 12.7 cm long SF57 glass rods and then sent to alaser-scanning microscope. The power of pump and Stokes beam beforemicroscope was controlled to be 20 mW and 200 mW, respectively. A 60×water immersion objective (NA=1.2, UPlanApo/IR™, Olympus) was used tofocus the light on the sample, and an oil condenser (NA=1.4, U-AAC,Olympus) was used to collect the signal. For hyperspectral SRS imaging,a 50-image stack was acquired at different pump-Stokes temporal delay,which was controlled by tuning the optical path difference between pumpand Stokes beam through a translation delay stage. Raman shift wascalibrated using standard samples, including DMSO, oleic acid, andlinolenic acid.

To achieve large-area mapping, samples were fixed on a motorized stage(PH117, Prior Scientific). A lab built LabView based program was used tocontrol moving of the stage and stitching of images. The stage moved toadjacent location with partial overlap after a hyperspectral SRS imagewas acquired at a current location. A montage image composed of 5×5individual 400×400-pixel images was acquired at each area of interest.The size of the montage image is approximately 500×500 μm. The pixeldwell time was set as 10 μs. For each sample, at least 3 montage imageswere acquired at different area of interest.

Spectral Phasor and CellProfiler™ Based Single Cell Analysis

The acquired large-area hyperspectral SRS images were segmented throughSpectral phasor analysis modified from previously published method [39].Spectral phasor was installed as a plugin in ImageJ. The images weretransformed into a two-dimension phasor plot based on Fourier Transform.Each dot on the phasor plot represents an SRS spectrum at a particularpixel. Pixels with similar spectra or chemical content were clustered onthe phasor plot. “Nuclei” and “lipid” images were generated by mappingthe corresponding clusters on the phasor plot back to two separateimages.

Lipid analysis in single cells were performed through the softwareCellProfiler™ [68]. The map of nuclei and cell images were input intoCellProfiler™ to outline each individual cells. The lipid map was inputinto CellProfiler™ to pick up the lipid droplet (LD) particles. Then,the lipid map was masked onto the outlined cell map to label the lipids.Morphological information of each cell and lipid analysis, including LDnumber and intensity in single cells were measured and reported in theoutput results. The total lipid intensity in each cell was plotted ashistogram graphs. For each sample, a few hundreds to a thousand of cellswere analyzed.

Isotope Labeling and SRS Imaging

For labeling with glucose-d₇, media was replaced with glucose-free DMEMmedium (Thermo Fisher Scientific, #11966025)+10% FBS+P/S supplementedwith 25 mM glucose-d₇ after seeding the cells in 35 mm glass-bottomdishes overnight. For labeling with FA or analogs, including PA-d₃₁,OA-d₃₄, and ODYA, FA or analogs were added to the culture media at finalconcentration of 100 μM and cells were treated for 6 h. For quantitativeSRS imaging, cells on glass-bottom dishes were fixed with 10% neutralbuffered formalin for 30 min and washed with PBS for 3 times.Hyperspectral SRS imaging was performed to the cells at Raman spectralregion from 2100 to 2300 cm⁻¹.

Reactive Oxidative Species Measurement

Cellular reactive oxidative species (ROS) was measured using afluorescent probe, 2′,7′-Dichlorofluorescin diacetate (DCFDA) (SigmaAldrich). Cells seeded in glass-bottom dishes were treated with orwithout 3.3 μM cisplatin for 3 h. DCFDA was added to the medium at finalconcentration of 10 μM and incubated for 15 min. After washing with PBSfor 3 times, cells were immediately imaged under confocal microscope(Zeiss LSM 700 microscope) with 488 nm as the exciting source. Laserpower was controlled at low setting to avoid fluorescence extinction.Images at ˜10 field of view were acquired for each sample.

Fatty Acid Oxidation Assay

Fatty acid oxidation (FAO) was measured using a commercial kit (Abcam,#ab217602) following the provided protocol. Briefly, cells were seededin 96-well plate with 150 k cells/well. After incubation overnight,medium was replaced. 10 μL of Extracellular O₂ consumption reagent, and2 drops of high-sensitivity mineral oil (pre-heated at 37° C.) wereadded to each well. Fluorescence was measured in plate reader at 2 minintervals for 180 min at excitation/emission=380/650 nm. Etomoxir wasadded at final concentration of 40 μM to block FAO. Oxygen consumptionrate (OCR) is presented as Δ_(Fluorescence intensity)/min/cell and FAOrate is calculated as OCR_(FAO)=OCR_(total)−OCR_(Etomoxir). At leastthree replicates were included for each measurement.

NADPH and ATP Assay

NADP/NADPH and ADP/ATP were measured by using commercial kits (Abcam,#ab65349 and #ab65313). For NADPH measurement, cells (˜1×10⁶ cells) werepelleted and extracted using NADPH/NADP extraction buffer. TotalNADP/NADPH was directly measured using the assay kit and NADPH alone wasmeasured after decomposing NADP by heating at 60° C. for 30 min.Absorption at 450 nm was measured by plate reader (Molecular Devices,SpectraMax i3x). For ATP measurement, cells were seeded in 96-wellplates. ATP was measured directly and total ATP+ADP was measured byconverting ADP to ATP. The luminescent signal was measured by platereader. At least three replicates were included for each measurement.

Cell Viability Assay

Cell viability was measured by MTS assay (Abcam, #ab197010) or byCellTiter-Glo™ assay (Promega, #G7570). Cells were seeded at 96-wellplates at densities of 2000˜5000 cells per well overnight. Treatment wasadded to the cells at indicated concentrations for 72 h. Cell viabilitywas measured by incubating with MTS reagent for 4 h and readingabsorbance at 490 nm or incubating with CellTier-Glo™ reagent for 10 minand reading luminescence by a plate reader. Six replicates were used foreach group.

Glucose Uptake Assay

Glucose uptake was measured using a fluorescent glucose analog2-deoxy-2-[(7-nitro-2,1,3-benzoxadiazol-4-yl) amino]-D-glucose (2-NBDG)(Cayman Chemicals). Cells seeded in glass-bottom dishes were incubatedwith 100 μM 2-NBDG for 2 h. Fluorescent images were taken by confocalmicroscope (Zeiss® LSM 700 microscope) with 488 nm laser as excitationsource. Images at ˜10 field of view were acquired for each sample.

Measurement of OCR and ECAR by Seahorse® Analysis

Cell lines were seeded in a Seahorse® XF96 Cell Culture Microplate(Agilent) at density of 6×10⁴ (OVCAR5 pair) or 4×10⁴ (PEO pair) perwell. After incubation at 37° C. overnight for cell attachment, OCR, andextracellular acidification rate (ECAR) were measured through Seahorse®XFe96 Analyzer (Agilent). Measurement time was 30 seconds following 3minutes mixture and 30 seconds waiting time. First three cycles wereused for basal respiration measurement. Effects of mitochondrialrespiration inhibitors 4 μM oligomycin, 4 μM FCCP, 25 μM rotenone, 50 μMantimycin A, 26.4 μM cisplatin, or 40 μM etomoxir on OC cells OCR andECAR were measured. Basal respiration, ATP production and maximalrespiration reduction were calculated following the manufacturer'sinstructions.

Reverse Transcription-PCR (RT-PCR)

Total RNA from ovarian cell lines were extracted via RNeasy® Mini Kit(Qiagen Inc.) and reverse transcribed by iScript® cDNA Synthesis Kit(Bio-Rad). RT-PCR was performed through StepOne Plus RT-PCR (AppliedBiosystems) using Power SYBR Green Master Mix (Thermo FisherScientific). Primer sequences, SEQ ID NOs: 1-24, are listed in Table 1.All procedures were following manufactures' instructions.

TABLE 1 Primer sequences used for RT-PCR measurement. Gene nameForward sequence Backward sequence CPT1a TCCAGTTGGCTTATCGTGGTGTCCAGAGTCCGATTGATTTTT SEQ ID NO: 1 GC SEQ ID NO: 2 FABP5TGAAGGAGCTAGGAGTGGGAA TGCACCATCTGTAAAGTTGCA SEQ ID NO: 3 G SEQ ID NO: 4FABP GGAAGGAAATAGCAACAGTGG TCCTACACGCTCACCATATAA (PM) SEQ ID NO: 5GC SEQ ID NO: 6 FATP1 CTTCGATGGCTATGTCAGCGA AGCACGTCACCTGAGAGGTAGSEQ ID NO: 7 SEQ ID NO: 8 FATP2 ATGCGAGAAAAGTTGGTGCTTTTCATCACGGACAGGTTCA SEQ ID NO: 9 SEQ ID NO: 10 FATP3ATACCTGGGAGCGTTTTGTG CCGCTGTCCTGTGTAGTTGA SEQ ID NO: 11 SEQ ID NO: 12FATP4 CTTTTCCAGCCGCTTCCACA TGGCTGGCAGGGAATGCA SEQ ID NO: 13SEQ ID NO: 14 FATP5 AGCTCCTGCGGTACTTGTGT AAGGTCTCCCACACATCAGCSEQ ID NO: 15 SEQ ID NO: 16 FATP6 GCTGGGCCTTATAAGCACACACAACCTCAGTGGTTGCGACA SEQ ID NO: 17 SEQ ID NO: 18 CD36GGCTGTGACCGGAACTGTG AGGTCTCCAACTGGCATTAGA SEQ ID NO: 19 A SEQ ID NO: 20FABP4 ACTGGGCCAGGAATTTGACG CTCGTGGAAGTGACGCCTT SEQ ID NO: 21SEQ ID NO: 22 PPIA CCCACCGTGTTCTTCGACATT GGACCCGTATGCTTTAGGATGSEQ ID NO: 23 A SEQ ID NO: 24

RT-PCR reaction generated a melting curve, and cycle threshold (Ct) wasrecorded for the gene of interest and house-keeping control gene (PPIA).The relative RNA expression level was calculated as ΔCt and normalizedby subtracting the Ct value of target gene from that of control gene.Results are presented as means+SD. Measurements were performed inbiological triplicate and each biological replicate includes threetechnical replicates.

Western Blot

Proteins were extracted from cell culture by RIPA lysis buffer (SigmaAldrich) with protease and phosphatase inhibitor cocktail and samplereducing agent (Thermo Fisher Scientific). Proteins were separated inBolt™ Bis-Tris Plus gels (Thermo Fisher Scientific) through gelelectrophoresis and transferred to PVDF membrane (Bio-Rad). Afterblocking in 5% non-fat milk (Bio-Rad) for 1 hour at room temperature,membranes were incubated with primary antibodies (CPT1 a (1:1000)(Proteintech Cat#: 15184-1-AP; RRID: AB_2084676) and GapDH (1:2000)(Proteintech; Cat#: 60004-1-Ig; RRID: AB_2107436; Clone#: 1E6D9)overnight at 4° C. followed by secondary anti-mouse antibodies (1:10000)(Proteintech; Cat#: SA00001-1; RRID: AB_2722565) for 1 hour at roomtemperature. Protein bands were developed by ECL reagent (Thermo FisherScientific) and detected through ChemiDoc MP imaging system (Bio-Rad).The band intensity was determined using ImageJ. Full scan blots are inthe Source Data file (not shown).

RNA Sequencing Analysis

RNA-seq data from OVCAR-5 and SKOV-3 cisplatin-resistant versus parentalcells were downloaded from the Gene Expression Omnibus with accessionID: GSE148003 [40]. Raw data were normalized with the R package edgeR[69]. Overlapping genes in the Hallmark Fatty Acid Metabolism gene setbetween OVCAR5-cisR versus parental cells and SKOV3-cisR versus parentalcells were used for generating heatmaps using the R package heatmap.Specifically, the heatmap of hierarchical clustering was generated forOVCAR5-cisR versus parental cells by using normalized counts. The samegene order after hierarchical clustering was applied to produce aheatmap for SKOV3-cisR versus parental cells.

Quantification and Statistical Analysis

All the data are presented as means±SD unless otherwise specified. Thestatistical significance was analyzed using two-tailed Student's t test.All experiments were repeated at least 3 times. N is indicated samplesize for each experiment. P<0.05 was considered statistically different.Statistical parameters can be found in figure legends. Data was analyzedand qualified by ImageJ, MATLAB, and Microsoft Excel. Origin was usedfor figure generation.Data Availability

The RNA-seq data used in this paper are available in the Gene ExpressionOmnibus with accession ID: GSE148003[https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE148003]

Example 1

High-Throughput SRS Imaging Unveils Lipid Accumulation in Ovarian CancerCells with Platinum Resistance

To identify the altered lipid metabolism in cisplatin-resistant cells, ahigh-throughput single-cell analysis approach was established thatcouples large-area hyperspectral SRS scanning of 200-500 cells per groupwith spectral phasor segmentation and CellProfiler™ analysis. As shownin FIG. 1A, a stack of large-area hyperspectral SRS images were acquiredcontaining hundreds of individual cells in each field-of-view. An SRSspectrum was extracted at each pixel from the image stack. Then, thehyperspectral SRS images were segmented through a spectral phasoralgorithm to generate maps of intracellular compartments correspondingto nuclei and lipids (mostly in lipid droplets) based on the spectrumsimilarity [39]. Next, the nuclei map was inputted into CellProfiler™ toguide the identification of the edges of each individual cell from theraw whole cell image. After individual cells were outlined, the lipidmap was mapped back to the corresponding cells. Lipids were color-codedbased on the colors of their parental cells. Finally, quantitativecharacterization of lipids in terms of integrated intensity, meanintensity, area, and lipid droplet size in each individual cell wasperformed. The image area is 500 μm by 500 μm.

To explore the lipid metabolic signature of cisplatin-resistant OCcells, isogenic pairs of cisplatin-resistant cells from three OC celllines were generated, including SKOV3, OVCAR5, and COV362, throughrepeated long-time exposures and recoveries after cisplatin treatment atIC₅₀ concentration [40]. Resistance to cisplatin in these cell lines wasvalidated by repeat assays measuring cisplatin dose response. All theresistant cell lines exhibited 2-3-fold increase of IC₅₀ when comparedto their parental counterpart cell lines (FIGS. 1B-1E). In addition,isogenic PEO1/PEO4 cell lines derived from the same patient werestudied, at the time of a platinum sensitive (PEO1) and platinumresistant-recurrence (PEO4) [41].

Using the high-throughput imaging analysis platform, the lipidmetabolism in these four pairs of cisplatin-resistant and parentalovarian cancer cell lines were analyzed. Comparison of SRS images ofsensitive PEO1 and cisplatin-resistant PEO4 cells showed an increase oflipid intensity in PEO4 cells, but with large cell-to-cell variations(FIG. 1F). The integrated lipid intensity in individual cells wasquantitatively analyzed and plotted in histograms. The histogramsdisplayed two distinct subpopulations, lipid-poor and lipid-rich, ineach cell line, indicating metabolic heterogeneity within the samegroup. While lipid-poor cells dominated in PEO1 cell line, PEO4 cellsshowed a dramatic increase in lipid-rich subpopulation and a decrease inlipid-poor subpopulation (FIG. 1G). Single-cell analysis revealed aneven more obvious increase in lipid-rich subpopulation and decrease inlipid-poor subpopulations in SKOV3-cisR cells, compared to SKOV3 (FIGS.1H and 1I). Additionally, similar lipid content changes were observed inthe other two pairs of cell lines, OVCAR5 versus OVCAR5-cisR (FIG. 1J),and COV362 versus COV362-cisR (FIG. 1K), supporting thatcisplatin-resistant cells harbor higher levels of lipid accumulation.Furthermore, after acute treatment with cisplatin, a significantincrease in lipid-rich subpopulation, and decrease of lipid-poorsubpopulation was found in SKOV3 cells (FIG. 1L), but no obvious changeof lipid distribution pattern in SKOV3-cisR cells were detected (FIG.1M), supporting that lipid-rich cells are more resistant to cisplatintreatment.

To determine whether lipid accumulation also occurs in vivo inplatinum-treated tumors, SRS imaging of lipids was performed in OVCAR5xenografts collected from mice treated weekly with saline or carboplatinfor three weeks. Tumor growth was suppressed by carboplatin treatment(FIG. 1N). However, cells isolated from xenografts residual aftercarboplatin treatment showed increased resistance to carboplatin in invitro treatment, compared to cells isolated from the saline treatedtumors. (FIG. 1O). The results shown in FIGS. 1P-1Q indicateheterogenous lipid accumulation and higher lipid amount in thecarboplatin-treated tumors compared with the platinum-sensitive tumors.Collectively, higher level of lipid content was a metabolic feature ofcisplatin-resistant ovarian cancer cells.

Example 2

Increased Fatty Acid Uptake but not De Novo Lipogenesis Contributes toHigh-Level Lipid Content in Cisplatin-Resistant Ovarian Cancer Cells

To identify the source of increased lipid content in cisplatin-resistantovarian cancer cells, the contribution of de novo lipogenesis and offatty acid uptake were examined, respectively. Using a stable isotopeprobing method [35], the level of lipogenesis was examined by feedingthe cells with deuterium labeled glucose-d₇. Newly synthesizedmacromolecules (mostly lipids) were imaged by hyperspectral SRSmicroscopy at Raman shift from 2050 cm′ to 2350 cm-1, covering thevibrational frequencies of C-D bonds. SRS images showed weaker C-Dsignal in cisplatin-resistant PEO4 cells than the signal in parentalPEO1 cells (FIG. 2A). Quantitative analysis confirmed significantreduction of both signal intensity and relative area fraction in PEO4cells when compared to PEO1 cells (FIG. 2B), indicating a decrease inglucose derived anabolism and de novo lipogenesis in cisplatin-resistantcells. Using a similar approach, the fatty acid uptake was examined byhyperspectral SRS imaging at C-D vibrational frequencies in cells fedwith deuterium labeled palmitic acid-d₃₁ (PA-d₃₁). In contrast toglucose-d₇ fed cells, C-D signal in PA-d₃₁ fed PEO4 cells was strongerthan PEO1 cells (FIG. 2C). Quantitative analysis revealed significantincrease of both signal intensity and relative area fraction (FIG. 2D).In addition to saturated FA (PA-d₃₁), the uptake of an unsaturated fattyacid, oleic acid-d₃₄ (OA-d₃₄) was tested. OA-d₃₄ uptake wassignificantly increased in PEO4 cells compared to PEO1 cells (FIGS. 2Eand 2F). These results indicate that the increased uptake of fatty acidwas not specific to a certain type of fatty acid, but rather reflects ageneral upregulation of fatty acid uptake pathway.

To verify if the observed phenomenon was cell type specific, themeasurements were repeated in SKOV3 and SKOV3-cisR cells. Consistently,SRS images and quantitative analysis showed a significant decrease inglucose-d₇ derived C-D signal in SKOV3-cisR cells (FIG. 2G) and increasein PA-d₃₁ signal (FIG. 2H) and OA-d34 signal in SKOV3-cisR cells (FIG.2I), when compared to parental SKOV3 cells. Additionally, the same trendwas observed in the other two pairs of cell lines, OVCAR5 versusOVCAR5-cisR (FIG. 2J) and COV362 versus COV362-cisR (FIG. 2K). Inaddition, inhibition of de novo lipogenesis by the FASN inhibitor C-75did not affect the increased lipid content in SKOV3-cisR compared withSKOV3, indicating that the enhanced lipid amount in cisplatin resistantcells is independent of de novo lipogenesis (FIGS. 2L and 2M). Thesedata collectively suggest a metabolic change from glucose derivedanabolism to fatty acid uptake in cisplatin-resistant ovarian cancercells.

Example 3

Metabolic Index as a Predictor of Cisplatin Resistance

Having shown decreased glucose-derived anabolism and increased fattyacid uptake in cisplatin-resistant ovarian cancer cells, whether thismetabolic feature can be used for differentiation of cisplatin-resistantfrom cisplatin-sensitive cancer cells was explored. To quantitativelycharacterize resistance to cisplatin, IC₅₀ dose of cisplatin in variouscell lines and their C-D intensities (presented as area fraction) fromglucose-d₇, PA-d₃₁, or OA-d₃₄ were calculated (Table 2). In Table 2below, quantitation of glucose-d₇ (G-d₇), PA-d₃₁, and OA-d₃₄ are shownas area fraction of C-D signal out of total cellular area, in meanvalues.

TABLE 2 Summary of quantitation results of glucose-d₇, PA-d₃₁, andOA-d₃₄ and IC₅₀s for cisplatin in 4 pairs of parental andcisplatin-resistant ovarian cancer cells. IC₅₀ for cisplatin Intensity(area fraction (%) Cell lines (μM) G-d₇ PA-d₃₁ OA-d₃₄ PE01 4.72 3.886.11 17.25 PE04 13.57 0.93 13.69 21.61 SKOV3 10.07 3.39 9.88 6.24SKOV3-CisR 17.29 0.84 15.79 10.97 OVCAR5 8.26 3.64 10.37 18.35OVCAR5-CisR 17.43 2.51 15.91 23.28 COV362 7.19 3.14 4.44 31.00COV362-cisR 15.17 2.03 14.88 41.43

Interestingly, glucose-d₇ derived C-D intensity was found negativelycorrelated to IC₅₀ to cisplatin (FIG. 3A), while PA-d₃₁ intensity waspositively correlated to IC₅₀ to cisplatin (FIG. 3B). To integrate twomeasurements into one, the ratio of PA-d₃₁/(PA-d₃₁+Glucose-d₇) was usedto give a dimensionless number ranging from 0 to 1. This ratio wasdefined as the “metabolic index”. The index linearly correlated to theIC₅₀ to cisplatin (FIG. 3C), providing the ability to detect andquantitatively determine resistance to cisplatin in cancer cells.

Understanding the value of this metabolic imaging method for detectingcisplatin resistance at the single cell level, hyperspectral SRS imagingwas applied to measure glucose derived anabolism and fatty acid uptakesimultaneously in the same cells cultured with Raman probes of fattyacid and glucose. Specifically, glucose-d₇ was used to follow glucoseanabolism [35, 36]. Instead of using deuterium labeled fatty acid totrace fatty acid uptake, a fatty acid analog was used, 17-octadecynoicacid (ODYA). ODYA has an endogenous C≡C at one end of the FA chain,which produces a strong Raman peak around 2100 cm⁻¹ (FIG. 3D). Thedistinctive Raman spectrum of ODYA enables spectral separation of C≡Clabeled fatty acid (from fatty acid uptake) from C-D labeledmacromolecules derived from glucose-d₇ (FIG. 3D). To test this in abiological environment, hyperspectral SRS imaging was performed in cellsfed with both ODYA and glucose-d₇. Two signals were observed withdistinctive spectra, from C≡C labeled fatty acid and C-D derived fromglucose-d₇, respectively (FIG. 3E).

Next, this approach was applied to image OVCAR5 and OVCAR5-cisR cells.Two components, C≡and C-D, were segmented from the raw SRS images basedon the spectral phasor algorithm³⁹ . Consistently, stronger C≡C signaland weaker C-D signal were observed in OVCAR5-cisR cells, when comparedto OVCAR5 cells (FIG. 3F). Quantitative analysis confirmed a significantincrease of C≡C and decrease of C-D signal. The metabolic index, ratioof C≡C/(C≡C +C-D), was more significantly increased in OVCAR5-cisR cells(FIG. 3G). Following this validated protocol, metabolic indices wereanalyzed in the other three pairs of cell lines, including PEO1 and PEO4(FIGS. 3H and 3I), SKOV3 and SKOV3-cisR (images not shown), and COV362and COV362-cisR cells (images not shown). Consistently, a linearcorrelation was established between metabolic index and IC_(50S) ofcisplatin in these cell lines (FIG. 3J).

To further validate the metabolic index as a predictor of platinumresistance in clinically relevant samples, this method was applied toprimary ovarian cancer cells obtained from de-identified consentingpatients for whom data on resistance/response to platinum was available.Patients' characteristics are included in Table 3 below. Tumor specimenswere obtained at the time of cytoreductive surgery either upfront (n=4)or after neoadjuvant chemotherapy (n=7). Platinum resistance was definedas disease recurring within six months from completing carboplatin basedchemotherapy, as assessed clinically, by CA125 criteria or CT scans.(n=11 patients).

TABLE 3 Patient characteristics for primary cells used for metabolicindex calculation. Carboplatin Sensitive Carboplatin Resistant PatientChemotherapy Patient Chemotherapy ID before surgery ID before surgery 1Yes 8 Yes 2 Yes 9 Yes 3 No 10 Yes 4 No 11 Yes 5 Yes 6 No 7 Yes

As shown in FIG. 3K, in ovarian cancer cells isolated from a patientwith platinum-sensitive disease, signal from ODYA was observed only insome of the cells, but the signal from glucose-d₇ was relatively strong.In ovarian cancer cells isolated from cisplatin-resistant tumors, ODYAsignal was more evenly distributed in the cells imaged, while signalfrom glucose-d₇ was weaker. Quantitative analysis showed that themetabolic indices were higher in samples from four patients withresistant tumors, when compared to cancer cells from seven patients withsensitive disease (FIG. 3L). The histogram of metabolic index dataindicated a clear separation between the sensitive and resistant groups(FIG. 3M). Receiver operating characteristic (ROC) analysis yielded athreshold value at 0.412 with high sensitivity of 1, specificity of 1and AUC (area under curve) of 1, suggesting that the metabolic index hasa high chance to successfully distinguish platinum sensitive andresistant ovarian cancer cells (FIG. 3N). This study shows clinicalapplicability of metabolic imaging for predicting response/resistance toplatinum.

Example 4

Fatty Acid Uptake Contributes to Cisplatin Resistance

Knowing that cisplatin-resistant ovarian cancer cells uptake more fattyacid, whether the fatty acid uptake was a cause or result of cisplatinresistance was investigated. First, whether modulating exogenous fattyacid availability affect endogenous lipid amount in cisplatin-resistantovarian cancer cells was tested. OVCAR5-cisR cells were cultured inlipid-deficient culture medium or normal medium supplemented with 1%lipid mixture for 24 hours and then examined lipid amount by SRSmicroscopy. Lipid-deficiency significantly reduced intra-cellular lipidamount while lipid supplementation increased the intra-cellular lipidamount (FIGS. 4A and 4B). Similar phenomenon was observed in theSKOV3-cisR cell line (FIGS. 4C and 4D). These observations are furthersupport that fatty acid uptake, instead of de novo lipogenesis, was themajor source of the lipid accumulation in cisplatin-resistant ovariancancer cells.

Next, whether modulating exogenous lipid availability impacts cancercell's resistance to cisplatin was examined. Lipid-deficiency increasedsensitivity to cisplatin, while lipid supplementation slightly decreasedsensitivity to cisplatin in OVCAR5-cisR (FIG. 4E), PEO4 (FIG. 4F), andSKOV3-cisR cells (FIG. 4G). To check whether ovarian cancer cells wouldupregulate glucose metabolism in a fatty acid depleted environment,glucose uptake was measured in SKOV3 and SKOV3-cisR cells cultured withregular or lipid-deficient medium using fluorescent glucose analog2-NBDG. Glucose uptake remained similar in lipid sufficient anddeficient environment (FIGS. 4H and 4I). Thus, resistance to cisplatincan be alleviated by modulating exogenous fatty acid availability.

Fatty acid uptake is a cellular process facilitated by multiple fattyacid transporters/carriers, including CD36, FATPs, and FABPs [42, 43].One of the key proteins reported to be upregulated in ovarian cancer isthe fatty acid binding protein 4 (FABP4) [12, 44]. Whether the increasedfatty acid uptake in cisplatin-resistant ovarian cancer cells wasregulated through upregulation of FABP4 was assessed, and showed verylow FABP4 mRNA levels in OVCAR5 and OVCAR5-cisR cells, suggesting FABP4likely does not play a major role in mediating the increased fatty aciduptake in cisplatin-resistant OVCAR5 cells. Next, the expression of apanel of other fatty acid uptake regulator genes was investigated,including CD36, FATP1-6, FABP5, and GOT2 (FABP(PM)) [45] and expressionof FABP5 and FABP(PM) was found to be higher than other genes (FIG. 4J).Replication experiments confirmed a significant upregulation of FABP5and FABP(PM) in resistant OVCAR5-cisR (FIGS. 4K-4L) and PEO4 cells (FIG.4M) compared to their parental cells, suggesting that FABP5 and FABP(PM)may mediate fatty acid uptake in cisplatin-resistant cells. In addition,cisplatin treatment induced an acute rise of FABP5 and FABP(PM)expression in cisplatin sensitive cell OVCAR5 (FIG. 4N), furthersupporting the involvement of FABP5 and FABP(PM) in cisplatin resistancerelated fatty acid uptake. In contrast, mRNA expression levels ofglucose transporter GLUT1 was reduced in resistant SKOV3-cisR comparedto parental cells (FIG. 4O). GLUT1 expression was not significantlychanged after cisplatin treatment in OVCAR5 cells, implying that GLUT1downregulation may be an adaptive change in cisplatin-resistant cellsrather than an acute response to cisplatin treatment (FIG. 4P). To ruleout the possibility that the increased fatty acid uptake was caused bychange in membrane fluidity at high concentrations of exogenous fattyacids, SRS imaging of fatty acid uptake was performed at lowerconcentrations of PA-d³¹ and OA-d³⁴, which showed a similar trend ofincreased fatty acid uptake in resistant cells (FIGS. 4Q-4T). These datasupport that increased fatty update in cisplatin-resistant ovariancancer cells is transporter mediated and likely due to an adaptivemetabolic reprogramming in response to cisplatin treatment.

Next, whether a potent inhibitor of FABP, BMS309403 (BMS), can suppressfatty acid uptake in cisplatin-resistant cells was tested [46].Treatment with BMS significantly reduced PA-d₃₁ uptake in OVCAR5-cisRcells (FIGS. 4U and 4V). Suppression of fatty acid uptake by BMS wasalso observed in SKOV3-cisR cells in a dose dependent manner (FIGS. 4Wand 4X). Furthermore, inhibition of fatty acid uptake by BMS reducedresistance to cisplatin in multiple resistant cell lines, including PEO4(FIG. 4Y), SKOV3-cisR (FIG. 4Z), and OVCAR5-cisR cells (FIG. 4AA), whilethe effects of BMS were less obvious in the sensitive cell lines (FIGS.4BB-4DD). These results support deregulated fatty acid uptake in thedevelopment of cisplatin resistance in OC cells.

Example 5

Increased Fatty Acid Oxidation Rate Contributes to Cisplatin Resistance

Considering that one major function of lipids is energy productionthrough fatty acid oxidation, whether fatty acid oxidation was increasedin cisplatin resistant cancer cells was investigated. By measuring theOCR in parental and resistant cancer cells, OVCAR5-cisR cells displayedmuch higher levels of oxygen consumption than OVCAR5 cells (FIG. 5A).Etomoxir, an inhibitor of CPT1 that transports fatty acid intomitochondria for fatty acid oxidation, was used to test whether theincreased oxidation rate arises from fatty acid oxidation. Etomoxir didnot induce an obvious change in oxygen consumption in OVCAR5 cells (FIG.5B), but significantly reduced oxygen consumption in OVCAR5-cisR cells(FIG. 5C). Quantitation of OCR confirmed a significant reduction of OCRafter etomoxir treatment in OVCAR5-cisR cells, but not in OVCAR5 cells(FIG. 5D). Fatty acid oxidation was also measured through the Seahorse®FAO assay to assess etomoxir-induced mitochondrial respiration change.As shown in FIG. 5E, OVCAR5-cisR cells had an overall higher OCR thanthe parental cells and showed an obvious reduction of OCR aftertreatment with etomoxir. In contrast, OCR of OVCAR5 cells was lesssensitive to etomoxir treatment. Etomoxir-induced basal respiration, ATPproduction, and maximal respiration reduction in resistant OVCAR5-cisRcells were significantly higher than those in sensitive OVCAR5 cells,suggesting significantly upregulated fatty acid oxidation in resistantcells (FIG. 5F). Further, Seahorse® measurement of OCR in PEO1 and PEO4cells supports a significant increase of fatty acid oxidation rate inPEO4 cells compared to PEO1 cells (FIG. 5G). These data indicate thatFAO significantly increases in cisplatin-resistant cells.

To test whether the increased fatty acid oxidation contributes tocisplatin resistance, the response to etomoxir in cisplatin-resistantcells and parental cells was investigated. Higher sensitivity toetomoxir treatment in cisplatin-resistant cell lines was observed whencompared to their parental cell lines, in paired cell lines includingPEO1 and PEO4 (FIG. 5H), OVCAR5 and OVCAR5-cisR (FIG. 5I), and COV362and COV362-cisR (FIG. 5J), indicating higher dependence on fatty acidoxidation in cisplatin-resistant cells. Next, whether etomoxir treatmentcould reduce resistance to cisplatin was tested. The dose-response tocisplatin curves were significantly left shifted with etomoxir treatmentin PEO4 (FIG. 5K), OVCA5-cisR (FIG. 5L), and COV362-cisR cells (FIG.5M), and support the potential of etomoxir to re-sensitize resistantovarian cancer cells to cisplatin. The observation was further confirmedby shRNA-mediated knockdown of CPT1a in OVCAR5-cisR cells (FIGS. 5N and5O). Knockdown of CPT1a in OVCAR5-cisR cells increased its sensitivityto cisplatin treatment compared to the control group (FIG. 5P).Interestingly, the CPT1a mRNA and protein levels were similar in OVCAR5and OVCAR5-cisR cells (FIGS. 5Q and 5R), indicating that enhanced fattyacid oxidation in cisplatin-resistant ovarian cancer is likely theresult of increased activation (not upregulation) of CPT1a.

To determine whether the functional changes were caused bytranscriptional reprogramming, RNA sequencing compared resistant andparental OVCAR5 and SKOV3 cells. Heatmaps show hierarchical clusteringof genes related to FA metabolism and illustrate distinct separationbetween sensitive/resistant cells (FIGS. 5S and 5T). In both cell lines,several FAO related genes including CRAT, PPARA, ACOT8, HSD17B10,ACADVL, ACOX1 and DECR1 were upregulated in resistant cells, while FAsynthesis related genes such as ME1, NSDHL, DHCR24, FASN, ELOVL5,ALDH3A2, ACSL4 and SERINC1 were downregulated (FIGS. 5S and 5T).

These transcriptomic findings show increased fatty acid oxidationactivity and reduced de novo fatty acid synthesis in cisplatin-resistantovarian cancer.

A patient-derived xenograft (PDX) model rendered platinum resistantthrough repeated exposure to carboplatin in vivo as described previously[40] was used to test whether interruption of fatty acid oxidation couldsensitize ovarian tumors to platinum in vivo. To avoid toxicity inducedby cisplatin, cisplatin was substituted with carboplatin, asecond-generation agent. Carboplatin or etomoxir single-agent treatmentinduced a slight reduction of tumor growth, whereas the combinationtreatment caused a significant suppression of tumor growth (FIG. 5U).Body weights remained stable in all groups, suggesting that thecombination treatment was tolerable (FIG. 5V). These data collectivelysupport development of a combination of platinum with a fatty acidoxidation inhibitor for platinum-resistant cancer treatment.

Example 6

FAO Facilitates Cancer Cell Survival Under Cisplatin-Induced OxidativeStress

Cisplatin has been known to cause cytotoxicity by inducing oxidativestress, in addition to DNA adduct formation [47-49]. Excess oxidativestress can inhibit glycolysis by inactivating key glycolytic enzymes,such as pyruvate kinase M2 (PKM2) and glyceraldehyde 3-phosphatedehydrogenase (GAPDH) [15, 50]. Increased reactive oxidative species(ROS) oxidizes intracellular NADPH and thus suppresses de novolipogenesis, as NADPH is one of the precursors for lipogenesis [51].Therefore, fatty acid uptake and oxidation may promote cancer cellsurvival under cisplatin-induced oxidative stress by replenishing freefatty acid and ATP, deficiency of which is caused by decreased de novolipogenesis and glycolysis under oxidative stress. As a test, theoxidative stress level was examined by measuring intracellular ROS usinga fluorescent probe, 2′,7′-dichlorodihydrofluorescein diacetate(H2DCFDA). Using confocal microscopy, OVCAR5-cisR cells showed muchstronger fluorescent signal than OVCAR5 cells (FIGS. 6A-6B). Similartrend of increased ROS in PEO4 cells compared to PEO1 cells was alsoobserved (FIGS. 6C-6D). Furthermore, the change in ROS in OVCAR5 andOVCAR5-cisR cells treated with cisplatin was analyzed and showed thatcisplatin treatment induced significant increase of ROS production inboth cell lines (FIG. 6E).

Next, whether the reduced form of intracellular NADPH is depleted incisplatin-resistant cells was examined. NADPH/NADP⁺ ratios weresignificantly lower in cisplatin-resistant PEO4 (FIG. 6F) andOVCAR5-cisR cells (FIG. 6G), when compared to PEO1 and OVCAR5 cells,respectively. The reduced NADPH level corroborate the SRS images showingdecreased de novo lipogenesis in cisplatin-resistant cells. Changes inglycolysis were analyzed by measuring ECAR by Seahorse®. Cisplatintreatment promptly lowered the ECAR rate in both PEO1 and PEO4 cells(FIG. 6H), reaching significant reduction within ˜30 min of treatment(FIG. 6I). On the contrary, cisplatin treatment also induced slightincrease of OCR in PEO1 and PEO4 cells (FIGS. 6J and 6K). In agreementwith the observed decreased glycolysis, glucose uptake, measured by2-NBDG under a confocal microscope, was reduced in cisplatin-treatedOVCAR5 cells (FIGS. 6L and 6M). Further, OVCAR5-cisR cells took up muchless 2-NBDG than OVCAR5 cells (FIGS. 6L and 6M), implying a decreasedreliance on glucose metabolism in cisplatin-resistant OC cells.

With glycolysis suppressed by increased oxidative stress, it appearedthat ATP production would be impaired in cisplatin-resistant cells orcisplatin treated cells. The cellular ATP/ADP level was measured andshowed the ratio of ATP/ADP was significantly lower in both PEO4 (FIG.6N) and OVCAR5-cisR cells (FIG. 6O), when compared to PEO1 and OVCAR5cells, respectively. Furthermore, acute treatment with cisplatin reducedATP/ADP ratio in OVCAR5 cells, but not in OVCAR5-cisR cells.Supplementation with palmitic acid significantly increased the ATP levelin OVCAR5-cisR cells, but not in OVCAR5 cells (FIG. 6P). Collectively,these data suggest that cisplatin-resistant OC cells undergo metabolicreprogramming from glucose-dependent to fatty acid-dependent metabolism.This could be related to the propensity of ovarian tumors to grow anddisseminate in an adipocyte-rich microenvironment. Adipocytes have beenreported to provide fatty acids as energy source for ovarian cancercells [12] and undergo increased lipolysis in response to cisplatintreatment [52]. FIG. 6Q shows glycolysis and lipogenesis are inhibitedby cisplatin-induced oxidative stress, limiting the production of energyas well as the synthesis of free fatty acids. To survive and proliferateunder cisplatin-induced oxidative stress, cancer cells upregulate fattyacid uptake and oxidation as an alternative route of energy production.

Example 7

Cisplatin Treatment Induces a Transient Metabolic Shift Toward IncreasedFatty Acid Uptake in Multiple Types of Cancers

Understanding that increased FA uptake in cisplatin-resistant ovariancancer cells is likely a stable metabolic adaptation to cisplatininduced oxidative stress, whether the same metabolic shift occurs inother types of cancers upon cisplatin treatment was examined. Platinumis widely used across malignancies. Therefore, a few representativecancer cell lines were selected, including MIA PaCa-2 pancreatic cancer,A549 lung cancer, and MD-MBA231 breast cancer, to test whether acutecisplatin treatment changes the rate of fatty acid uptake. The IC₅₀ tocisplatin in these three cell lines was determined, and 6.6 μM wasselected as the final treatment concentration, at which dose nosignificant cell death was induced (FIGS. 7A-7C). Results show thattreatment with 6.6 μM cisplatin significantly increased uptake of PA-d₃₁and OA-d₃₄ in MIA PaCa-2 cells (FIGS. 7D and 7E). The fold increase inPA uptake was more significant than OA (FIGS. 7F), suggesting PA mightbe a preferred source of FA for cells under cisplatin induced oxidativestress. Similarly, we observed that treatment with cisplatin alsoinduced a significant increase in PA-d₃₁ and OA-d₃₄ uptake in A549(FIGS. 7G, 7H, and 7I) and MD-MBA231 cells (FIGS. 7J, 7K, and 7L). Thesefindings are broadly applicable to multiple types of cisplatin-resistantcancers. FIG. 8 illustrates the cellular metabolism switch fromglycolysis to fatty acid oxidation with decreased glucose uptake,glycolysis and de novo lipogenesis while fatty acid uptake and oxidationare increased. This shows a central metabolic alteration or change inanabolic and energetic metabolism in resistant cells. Inhibition of FAOre-sensitizes cisplatin-resistant OC cells to cisplatin treatment bothin vitro and in vivo, paving the foundation towards a new combinationaltherapy of FAO inhibitors and cancer therapies such as chemotherapyplatinum drugs.

REFERENCES

All references and publications cited in this disclosure specificationwith the examples and drawings are incorporated by reference herein intheir entireties, including:

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We claim:
 1. An assay for determining resistance in a target cell ortissue to a therapy associated with cellular stress, comprising,measuring with chemical microscopy a functional metabolic change in thetarget cell or tissue, and determining a metabolic index of resistancein the target cell or tissue to the therapy.
 2. The assay of claim 1,wherein the functional metabolic change comprises a change from glucoseand glycolysis dependent anabolism and energy metabolism to fatty aciduptake and fatty acid oxidation dependent anabolism and energymetabolism.
 3. The assay of claim 1, wherein the metabolic indexcorrelates to resistance to the therapy in the target cell or tissuewhen the functional metabolic change is a decrease in glucose andglycolysis dependent anabolism and an increase in fatty acid uptake andfatty acid oxidation dependent anabolism and energy metabolism.
 4. Theassay of claim 3, wherein the metabolic index further correlates toresistance to the therapy in the target cell or tissue when themetabolic change is a decrease in de novo lipogenesis in the target cellor tissue.
 5. The assay of claim 1, wherein the metabolic index is aratio of an increase in fatty acid uptake and oxidation to a decrease inglucose-dependent anabolism.
 6. The assay of claim 1, wherein measuringwith chemical microscopy functional metabolic change comprises measuringglucose and glycolysis derived anabolism in the target cell or tissue,measuring fatty acid uptake and oxidation in the target cell or tissue,and determining a change from glucose anabolism in the target cell ortissue to fatty acid uptake and oxidation energy metabolism.
 7. Theassay of claim 6, further comprising measuring de novo lipogenesis inthe target cell or tissue, and determining a change from glucose andglycolysis dependent anabolism and de novo lipogenesis in the targetcell or tissue to fatty acid uptake and oxidation energy metabolism. 8.The assay of claim 1, wherein the chemical microscopy comprises Ramanmicroscopy or infrared microscopy.
 9. The assay of claim 1, wherein thetarget cell induces cellular stress in the target cell or tissue. 10.The assay of claim 1, wherein the cellular stress in the target cell ortissue is oxidative stress, metabolic stress, hypoxic stress, nutrientstress, thermal stress, genotoxic stress, or combinations thereof. 11.The assay of claim 1, wherein the target cell or tissue is a cancercell, and the therapy is a cancer therapy.
 12. The assay of claim 11,wherein the cancer therapy is selected from chemotherapy, radiotherapy,immunotherapy, targeted therapy, hormone therapy, light or lasertherapy, photodynamic therapy, and combinations thereof.
 13. The assayof claim 12, wherein the chemotherapy is a platinum-based therapeuticselected from cisplatin, carboplatin, oxaliplatin, nedaplatin, andcombinations thereof.
 14. A method of treating or inhibiting resistancein a target cell or tissue to a therapy associated with cellular stressin a subject, comprising: performing the assay of claim 1 to determinethe metabolic index of resistance in the target cell or tissue to thetherapy; administering at least one inhibitor of fatty acid oxidation tothe subject; and administering at least one therapy to the subject. 15.The method of claim 14, wherein measuring with chemical microscopyfunctional metabolic change comprises measuring glucose and glycolysisderived anabolism in the cancer cell, measuring fatty acid uptake andoxidation in the target cell or tissue, and the metabolic index is theratio of an increase in fatty acid uptake and oxidation energymetabolism to a decrease in glucose and glycolysis derived anabolism inthe target cell or tissue.
 16. The method of claim 14, whereinperforming the assay of claim 1 further comprises obtaining a targetcell or tissue from the subject.
 17. The method of claim 14, wherein thetarget cell is a cancer cell, and the therapy is a cancer therapy. 18.The method of claim 14, wherein determining the metabolic index ofresistance in the target cell or tissue to the therapy further includesa decrease in de novo lipogenesis.
 19. The method of claim 14, whereinfatty acid oxidation is inhibited in the target cell or tissue and thetherapy induces cellular stress in the target cell or tissue, therebyinhibiting resistance to the therapy.
 20. The method of claim 14,wherein the at least one inhibitor of fatty acid oxidation is selectedfrom etomoxir, oxfenicine, perhexiline, mildronate, trimetazidine, andcombinations thereof.